DOCSLIB.ORG

Mouse Phc3 Conditional Knockout Project (CRISPR/Cas9)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mouse Phc3 Conditional Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Phc3 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Phc3 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Phc3 gene (NCBI Reference Sequence: NM_153421 ; Ensembl: ENSMUSG00000037652 ) is located on Mouse chromosome 3. 15 exons are identified, with the ATG start codon in exon 2 and the TAA stop codon in exon 15 (Transcript: ENSMUST00000168645). Exon 8 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Phc3 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-146F20 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 8 starts from about 29.73% of the coding region. The knockout of Exon 8 will result in frameshift of the gene. The size of intron 7 for 5'-loxP site insertion: 5252 bp, and the size of intron 8 for 3'-loxP site insertion: 4392 bp. The size of effective cKO region: ~1372 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 8 15 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Phc3 Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the GC Content Distribution Window size: 300 bp Sequence 12 Summary: Full Length(7872bp) | A(26.63% 2096) | C(19.58% 1541) | T(33.7% 2653) | G(20.1% 1582) Note: The sequence of homologous arms and cKO region is analyzed to determine the GC content. No significant high GC-content region is found. So this region is suitable for PCR screening or sequencing analysis. Page 3 of 8 https://www.alphaknockout.com BLAT Search Results (up) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 3000 1 3000 3000 100.0% chr3 - 30937407 30940406 3000 browser details YourSeq 127 1323 1534 3000 85.4% chr12 - 111253524 111253712 189 browser details YourSeq 125 1363 1539 3000 87.5% chr5 + 98959586 98959750 165 browser details YourSeq 122 1379 1539 3000 91.7% chr17 - 15560878 15561043 166 browser details YourSeq 118 1388 1883 3000 82.4% chr2 - 155291641 155292049 409 browser details YourSeq 115 1386 1539 3000 91.9% chr1 - 12999131 12999418 288 browser details YourSeq 114 1393 1534 3000 92.0% chr12 - 119256990 119257136 147 browser details YourSeq 113 1401 1534 3000 93.8% chr17 - 72655501 72655639 139 browser details YourSeq 109 1390 1538 3000 93.0% chr3 - 35412513 35412666 154 browser details YourSeq 109 1363 1516 3000 84.1% chr13 + 97374853 97374985 133 browser details YourSeq 108 1363 1514 3000 84.8% chrX - 144332551 144332676 126 browser details YourSeq 108 1383 1512 3000 91.2% chr12 - 75479163 75479290 128 browser details YourSeq 106 1402 1734 3000 83.4% chr17 + 88660820 88661080 261 browser details YourSeq 105 1388 1511 3000 89.2% chr1 - 13041758 13041877 120 browser details YourSeq 105 1393 1539 3000 92.8% chrX + 53827831 53827997 167 browser details YourSeq 105 1398 1540 3000 92.0% chr3 + 82737382 82737526 145 browser details YourSeq 104 1363 1511 3000 84.6% chr11 + 89004433 89004560 128 browser details YourSeq 102 1388 1512 3000 88.3% chr4 - 48481896 48482015 120 browser details YourSeq 102 1387 1511 3000 88.7% chr9 + 79817586 79817708 123 browser details YourSeq 101 1399 1534 3000 92.5% chr8 + 3257692 3257832 141 Note: The 3000 bp section upstream of Exon 8 is BLAT searched against the genome. No significant similarity is found. BLAT Search Results (down) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 3000 1 3000 3000 100.0% chr3 - 30933035 30936034 3000 browser details YourSeq 38 1111 1157 3000 86.4% chr7 - 123271022 123271066 45 browser details YourSeq 34 1080 1125 3000 85.8% chr12 - 99837673 99837717 45 browser details YourSeq 26 1088 1138 3000 76.5% chr11 + 116470600 116470651 52 browser details YourSeq 25 1511 1537 3000 96.3% chr12 - 89045711 89045737 27 browser details YourSeq 20 2833 2852 3000 100.0% chr8 - 109670283 109670302 20 browser details YourSeq 20 1102 1129 3000 85.8% chr13 + 44685300 44685327 28 Note: The 3000 bp section downstream of Exon 8 is BLAT searched against the genome. No significant similarity is found. Page 4 of 8 https://www.alphaknockout.com Gene and protein information: Phc3 polyhomeotic 3 [ Mus musculus (house mouse) ] Gene ID: 241915, updated on 10-Oct-2019 Gene summary Official Symbol Phc3 provided by MGI Official Full Name polyhomeotic 3 provided by MGI Primary source MGI:MGI:2181434 See related Ensembl:ENSMUSG00000037652 Gene type protein coding RefSeq status VALIDATED Organism Mus musculus Lineage Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Glires; Rodentia; Myomorpha; Muroidea; Muridae; Murinae; Mus; Mus Also known as Edr3; Hph3; E030046K01Rik Expression Ubiquitous expression in thymus adult (RPKM 7.9), genital fat pad adult (RPKM 7.0) and 28 other tissues See more Orthologs human all Genomic context Location: 3; 3 A3 See Phc3 in Genome Data Viewer Exon count: 17 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 3 NC_000069.6 (30899295..30969479, complement) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 3 NC_000069.5 (30798217..30868337, complement) Chromosome 3 - NC_000069.6 Page 5 of 8 https://www.alphaknockout.com Transcript information: This gene has 10 transcripts Gene: Phc3 ENSMUSG00000037652 Description polyhomeotic 3 [Source:MGI Symbol;Acc:MGI:2181434] Gene Synonyms E030046K01Rik, EDR3, HPH3 Location Chromosome 3: 30,899,371-30,969,415 reverse strand. GRCm38:CM000996.2 About this gene This gene has 10 transcripts (splice variants), 239 orthologues, 16 paralogues, is a member of 1 Ensembl protein family and is associated with 4 phenotypes. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Phc3- ENSMUST00000168645.7 10976 981aa ENSMUSP00000130142.1 Protein coding CCDS17288 Q8CHP6 TSL:1 209 GENCODE basic APPRIS P3 Phc3- ENSMUST00000108255.7 3468 948aa ENSMUSP00000103890.1 Protein coding CCDS50884 D3YY34 TSL:1 204 GENCODE basic APPRIS ALT2 Phc3- ENSMUST00000129817.8 3019 981aa ENSMUSP00000114916.2 Protein coding CCDS17288 Q8CHP6 TSL:1 206 GENCODE basic APPRIS P3 Phc3- ENSMUST00000177992.7 2920 948aa ENSMUSP00000136820.1 Protein coding CCDS50884 D3YY34 TSL:5 210 GENCODE basic APPRIS ALT2 Phc3- ENSMUST00000064718.11 2893 951aa ENSMUSP00000065617.5 Protein coding CCDS50885 B7ZNA5 TSL:1 202 GENCODE basic APPRIS ALT2 Phc3- ENSMUST00000099163.4 2808 715aa ENSMUSP00000096767.3 Protein coding - E9QPT4 TSL:1 203 GENCODE basic Phc3- ENSMUST00000046624.11 3491 595aa ENSMUSP00000037862.5 Nonsense mediated - F8WIN6 TSL:5 201 decay Phc3- ENSMUST00000152357.7 3155 232aa ENSMUSP00000117614.1 Nonsense mediated - D6REI6 TSL:1 208 decay Phc3- ENSMUST00000150939.2 3660 No - Retained intron - - TSL:3 207 protein Phc3- ENSMUST00000124472.1 2958 No - Retained intron - - TSL:1 205 protein Page 6 of 8 https://www.alphaknockout.com 90.05 kb Forward strand 30.90Mb 30.92Mb 30.94Mb 30.96Mb Genes Gpr160-207 >protein coding Gm2979-201 >processed pseudogene (Comprehensive set... Gpr160-203 >protein coding Gpr160-204 >protein coding Gpr160-201 >protein coding Gpr160-208 >protein coding Gpr160-202 >protein coding Gpr160-206 >protein coding Contigs AC111093.10 > Genes (Comprehensive set... < Phc3-209protein coding < Phc3-201nonsense mediated decay < Phc3-204protein coding < Phc3-208nonsense mediated decay < Phc3-206protein coding < Phc3-210protein coding < Phc3-202protein coding < Phc3-205retained intron < 9530022L04Rik-201TEC < Phc3-203protein coding < Phc3-207retained intron Regulatory Build 30.90Mb 30.92Mb 30.94Mb 30.96Mb Reverse strand 90.05 kb Regulation Legend CTCF Enhancer Open Chromatin Promoter Promoter Flank Transcription Factor Binding Site Gene Legend Protein Coding Ensembl protein coding merged Ensembl/Havana Non-Protein Coding pseudogene processed transcript Page 7 of 8 https://www.alphaknockout.com Transcript: ENSMUST00000168645 < Phc3-209protein coding Reverse strand 70.03 kb ENSMUSP00000130... MobiDB lite Low complexity (Seg) Superfamily Sterile alpha motif/pointed domain superfamily SMART Sterile alpha motif domain Pfam Sterile alpha motif domain PROSITE profiles Zinc finger, FCS-type Sterile alpha motif domain PANTHER PTHR12247:SF88 PTHR12247 Gene3D Sterile alpha motif/pointed domain superfamily FCS-type zinc finger superfamily CDD cd09577 All sequence SNPs/i... Sequence variants (dbSNP and all other sources) Variant Legend splice acceptor variant inframe deletion missense variant splice region variant synonymous variant Scale bar 0 100 200 300 400 500 600 700 800 981 We wish to acknowledge the following valuable scientific information resources: Ensembl, MGI, NCBI, UCSC. Page 8 of 8.
Load more

Recommended publications
  • Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
    University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia.
    [Show full text]
  • Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
    Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A.
    [Show full text]
  • Supplemental Material 1
    SUP. FIGURE S1 DAPI NFAT Merge DMSO Ac­5SGlcNAc Figure S1. Inhibition of OGT does not prevent nuclear translocation of NFAT. Jurkat cells !"#$%&'()*+(!!,-.'/012"#..(3'4056'7(+('"+(#"(3'7,"8'9:';<'5=29>/%=45='?+'@<>A'B?+'CD' 8+!E''F(%%!'7(+('"8(-'*%#"(3'?-'#-",2F@GHF@ID2=?#"(3'=?J(+!%,*!'B?+'G:'K,-!E''5B"(+'B,)#",?-L' =(%%!'7(+('#-#%&M(3'$&'=?-B?=#%'K,=+?!=?*&E SUP. FIGURE S2 Labeled Unlabeled Labeled Unlabeled 1h 18h ­Az ­PEG 1h 19h ­Az ­PEG !"#$%"#&'( ­ + ­ + + + !"#$%"#&'( ­ + ­ + + + 110 EWSR1 160 *34+&5 80 &,/ 160 )*+-2$ 160 110 )*+,& 80 60 &,/ 160 110 80 0"1"- RUNX1 110 80 60 60 50 160 SP1 160 110 ELF1 110 80 160 NUP98 110 80 )*+&-. &,/ Figure S2. PEG mass tags allow visualization of O­GlcNAc stoichiometry. 5K PEG mass tags were affixed to O­GlcNAc groups on proteins from control or activated T cells via enzy­ matic labeling with azide and copper­free click chemistry. Proteins were then analyzed for shifts in electrophoretic mobility by immunoblot. For the unlabeled control samples, either the azide (­Az) or PEG (­PEG) reagent was omitted during the labeling procedure. Note that HCFC1 appears as multiple bands because the protein is expressed as a single polypeptide that under­ goes proteolytic processing. UBAP2L appears as two bands in the unlabeled control samples due to alternative splicing. Table S1. Details of 133 higher confidence and 81 lower confidence O-GlcNAc glycoproteins1. Confidence Uniprot ID Symbol Specificity Chi Higher P55265 ADAR 100% 9.11E-04 Higher Q09666 AHNAK 92% 2.36E-16 Higher Q8IWZ3 ANKHD1 100% 1.19E-03 Higher
    [Show full text]
  • Retroviral Vector Integration Deregulates Gene Expression but Has No Consequence on the Biology and Function of Transplanted T Cells
    Retroviral vector integration deregulates gene expression but has no consequence on the biology and function of transplanted T cells Alessandra Recchia*†, Chiara Bonini*‡, Zulma Magnani*, Fabrizia Urbinati†, Daniela Sartori*§, Sara Muraro*, Enrico Tagliafico†, Attilio Bondanza*¶, Maria Teresa Lupo Stanghellini‡, Massimo Bernardi‡, Alessandra Pescarollo‡, Fabio Ciceri‡, Claudio Bordignon§¶, and Fulvio Mavilio†ʈ *Cancer Immunotherapy and Gene Therapy Program, and ‡Bone Marrow Transplantation Unit, Istituto Scientifico H. San Raffaele, Via Olgettina 58, 20132 Milan, Italy; †Department of Biomedical Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41100 Modena, Italy; §MolMed S.p.A., Via Olgettina 58, 20132 Milan, Italy; and ¶San Raffaele ‘‘Vita e Salute’’ University, Via Olgettina 58, 20132 Milan, Italy Edited by Malcolm A. Martin, National Institutes of Health, Bethesda, MD, and approved December 6, 2005 (received for review September 1, 2005) The use of retroviral vectors in gene therapy has raised safety is currently profiled to provide a graft-versus-leukemia effect to concerns for the genotoxic risk associated with their uncontrolled patients in relapse after HLA-identical HSC transplantation (14), insertion into the human genome. We have analyzed the conse- or to promote immune reconstitution and prevent relapse in quences of retroviral transduction in T cells from leukemic patients patients undergoing HLA-haploidentical HSC transplantation. In treated with allogeneic stem cell transplantation and donor lym- 46 patients treated since 1994 in both contexts, we were able to phocytes genetically modified with a suicide gene (HSV-TK). Ret- control GvHD in 100% of the cases, while preserving antiviral and roviral vectors integrate preferentially within or near transcribed antitumor activity (14–16).
    [Show full text]
  • Proteome-Wide Analysis of Protein Abundance and Turnover
    Wellcome Open Research 2018, 3:51 Last updated: 01 JUN 2018 RESEARCH ARTICLE Proteome-wide analysis of protein abundance and turnover remodelling during oncogenic transformation of human breast epithelial cells [version 1; referees: 2 approved, 1 approved with reservations] Tony Ly 1-3*, Aki Endo1,2,4*, Alejandro Brenes1,2, Marek Gierlinski1,2, Vackar Afzal1,2, Andrea Pawellek1,2, Angus I. Lamond 1,2 1Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK 2Laboratory for Quantitative Proteomics, University of Dundee, Dundee, UK 3Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK 4Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan * Equal contributors v1 First published: 02 May 2018, 3:51 (doi: 10.12688/wellcomeopenres.14392.1) Open Peer Review Latest published: 02 May 2018, 3:51 (doi: 10.12688/wellcomeopenres.14392.1) Referee Status: Abstract Background: Viral oncogenes and mutated proto-oncogenes are potent drivers of cancer malignancy. Downstream of the oncogenic trigger are Invited Referees alterations in protein properties that give rise to cellular transformation and the 1 2 3 acquisition of malignant cellular phenotypes. Developments in mass spectrometry enable large-scale, multidimensional characterisation of version 1 proteomes. Such techniques could provide an unprecedented, unbiased view published report report report of how oncogene activation remodels a human cell proteome. 02 May 2018 Methods: Using quantitative MS-based proteomics and cellular assays, we analysed how transformation induced by activating v-Src kinase remodels the 1 Jeroen Krijgsveld, German Cancer proteome and cellular phenotypes of breast epithelial (MCF10A) cells. SILAC Research Center, Germany MS was used to comprehensively characterise the MCF10A proteome and to measure v-Src-induced changes in protein abundance across seven 2 Chunaram Choudhary, University of time-points (1-72 hrs).
    [Show full text]
  • UNIVERSITY of CALIFORNIA Los Angeles a Sterile Alpha Motif
    UNIVERSITY OF CALIFORNIA Los Angeles A Sterile Alpha Motif Domain Network Involved in Kidney Development A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Biochemistry and Molecular Biology by Catherine Nicole Leettola 2015 ABSTRACT OF THE DISSERTATION A Sterile Alpha Motif Domain Network Involved in Kidney Development by Catherine Nicole Leettola Doctor of Philosophy in Biochemistry and Molecular Biology University of California, Los Angeles, 2015 Professor James U. Bowie, Chair Cystic kidney diseases including polycystic kidney disease (PKD) and nephronophthisis (NPHP) are the most common genetic disorders leading to end-stage renal failure in humans. Animal models and human cases of PKD and NPHP have implicated the sterile alpha motif (SAM) domain containing proteins bicaudal C homolog 1 (BICC1) and ankyrin repeat and SAM- domain containing protein 6 (ANKS6) as being involved in these conditions and important for renal development. SAM domains are known protein-protein interaction domains that are capable of binding each other to form polymers and heterodimers. Using a negGFP native gel assay, we have identified the SAM domain of the previously uncharacterized protein ankyrin repeat and SAM-domain containing protein 3 (ANKS3) as a direct binding partner of the BICC1 and ANKS6 SAM domains. We found the ANKS3 SAM domain to polymerize with moderate affinity and determined the ANKS6 SAM domain can bind to a single end of this polymer. Crystal structures of the ANKS3 SAM domain polymer and the ANKS3 SAM-ANKS6 SAM ii heterodimer are presented to reveal typical ML-EH SAM domain interaction interfaces with a pronounced charge complementarity.
    [Show full text]
  • The Potential Genetic Network of Human Brain SARS-Cov-2 Infection
    bioRxiv preprint doi: https://doi.org/10.1101/2020.04.06.027318; this version posted April 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 4.0 International license. The potential genetic network of human brain SARS-CoV-2 infection. Colline Lapina 1,2,3, Mathieu Rodic 1, Denis Peschanski 4,5, and Salma Mesmoudi 1, 3, 4, 5 1 Prematuration Program: linkAllBrains. CNRS. Paris. France 2 Graduate School in Cognitive Engineering (ENSC). Talence. France 3 Complex Systems Institute Paris île-de-France. Paris. France 4 CNRS, Paris-1-Panthéon-Sorbonne University. CESSP-UMR8209. Paris. France 5 MATRICE Equipex. Paris. France Abstract The literature reports several symptoms of SARS-CoV-2 in humans such as fever, cough, fatigue, pneumonia, and headache. Furthermore, patients infected with similar strains (SARS-CoV and MERS-CoV) suffered testis, liver, or thyroid damage. Angiotensin-converting enzyme 2 (ACE2) serves as an entry point into cells for some strains of coronavirus (SARS-CoV, MERS-CoV, SARS-CoV-2). Our hypothesis was that as ACE2 is essential to the SARS-CoV-2 virus invasion, then brain regions where ACE2 is the most expressed are more likely to be disturbed by the infection. Thus, the expression of other genes which are also over-expressed in those damaged areas could be affected. We used mRNA expression levels data of genes provided by the Allen Human Brain Atlas (ABA), and computed spatial correlations with the LinkRbrain platform.
    [Show full text]
  • 393LN V 393P 344SQ V 393P Probe Set Entrez Gene
    393LN v 393P 344SQ v 393P Entrez fold fold probe set Gene Gene Symbol Gene cluster Gene Title p-value change p-value change chemokine (C-C motif) ligand 21b /// chemokine (C-C motif) ligand 21a /// chemokine (C-C motif) ligand 21c 1419426_s_at 18829 /// Ccl21b /// Ccl2 1 - up 393 LN only (leucine) 0.0047 9.199837 0.45212 6.847887 nuclear factor of activated T-cells, cytoplasmic, calcineurin- 1447085_s_at 18018 Nfatc1 1 - up 393 LN only dependent 1 0.009048 12.065 0.13718 4.81 RIKEN cDNA 1453647_at 78668 9530059J11Rik1 - up 393 LN only 9530059J11 gene 0.002208 5.482897 0.27642 3.45171 transient receptor potential cation channel, subfamily 1457164_at 277328 Trpa1 1 - up 393 LN only A, member 1 0.000111 9.180344 0.01771 3.048114 regulating synaptic membrane 1422809_at 116838 Rims2 1 - up 393 LN only exocytosis 2 0.001891 8.560424 0.13159 2.980501 glial cell line derived neurotrophic factor family receptor alpha 1433716_x_at 14586 Gfra2 1 - up 393 LN only 2 0.006868 30.88736 0.01066 2.811211 1446936_at --- --- 1 - up 393 LN only --- 0.007695 6.373955 0.11733 2.480287 zinc finger protein 1438742_at 320683 Zfp629 1 - up 393 LN only 629 0.002644 5.231855 0.38124 2.377016 phospholipase A2, 1426019_at 18786 Plaa 1 - up 393 LN only activating protein 0.008657 6.2364 0.12336 2.262117 1445314_at 14009 Etv1 1 - up 393 LN only ets variant gene 1 0.007224 3.643646 0.36434 2.01989 ciliary rootlet coiled- 1427338_at 230872 Crocc 1 - up 393 LN only coil, rootletin 0.002482 7.783242 0.49977 1.794171 expressed sequence 1436585_at 99463 BB182297 1 - up 393
    [Show full text]
  • Vast Human-Specific Delay in Cortical Ontogenesis Associated With
    Supplementary information Extension of cortical synaptic development distinguishes humans from chimpanzees and macaques Supplementary Methods Sample collection We used prefrontal cortex (PFC) and cerebellar cortex (CBC) samples from postmortem brains of 33 human (aged 0-98 years), 14 chimpanzee (aged 0-44 years) and 44 rhesus macaque individuals (aged 0-28 years) (Table S1). Human samples were obtained from the NICHD Brain and Tissue Bank for Developmental Disorders at the University of Maryland, USA, the Netherlands Brain Bank, Amsterdam, Netherlands and the Chinese Brain Bank Center, Wuhan, China. Informed consent for use of human tissues for research was obtained in writing from all donors or their next of kin. All subjects were defined as normal by forensic pathologists at the corresponding brain bank. All subjects suffered sudden death with no prolonged agonal state. Chimpanzee samples were obtained from the Yerkes Primate Center, GA, USA, the Anthropological Institute & Museum of the University of Zürich-Irchel, Switzerland and the Biomedical Primate Research Centre, Netherlands (eight Western chimpanzees, one Central/Eastern and five of unknown origin). Rhesus macaque samples were obtained from the Suzhou Experimental Animal Center, China. All non-human primates used in this study suffered sudden deaths for reasons other than their participation in this study and without any relation to the tissue used. CBC dissections were made from the cerebellar cortex. PFC dissections were made from the frontal part of the superior frontal gyrus. All samples contained an approximately 2:1 grey matter to white matter volume ratio. RNA microarray hybridization RNA isolation, hybridization to microarrays, and data preprocessing were performed as described previously (Khaitovich et al.
    [Show full text]
  • 1 Transcriptomic Responses to Hypoxia in Endometrial and Decidual Stromal Cells 2 3 Kalle T
    bioRxiv preprint doi: https://doi.org/10.1101/2019.12.21.885657; this version posted December 23, 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. 1 Transcriptomic responses to hypoxia in endometrial and decidual stromal cells 2 3 Kalle T. Rytkönen 1,2,3,4, Taija Heinosalo 1, Mehrad Mahmoudian 2,5, Xinghong Ma 3,4, Antti 4 Perheentupa 1,6, Laura L. Elo 2, Matti Poutanen 1 and Günter P. Wagner 3,4,7,8 5 6 1 Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, 7 University of Turku, Kiinamyllynkatu 10, 20014, Finland 8 2 Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 9 20520, Turku, Finland 10 3 Yale Systems Biology Institute, West Haven, Connecticut 06516, USA 11 4 Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, 12 USA 13 5 Department of Future Technologies, University of Turku, FI-20014 Turku, Finland 14 6 Department of Obstetrics and Gynecology, Turku University Hospital, Kiinamyllynkatu 4-8, 15 20521, Turku, Finland. 16 7 Department of Obstetrics, Gynecology and Reproductive Sciences, Yale Medical School, New 17 Haven 06510, USA 18 8 Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI- 48201, USA 19 20 Correspondence should be addresses to K T Rytkönen; Email: [email protected]. Address: Institute 21 of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of 22 Turku, Kiinamyllynkatu 10, 20014, Finland / Turku Bioscience Centre, University of Turku and 23 Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland.
    [Show full text]
  • Supplementary Data
    SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial.
    [Show full text]
  • Pan-Cancer Multi-Omics Analysis and Orthogonal Experimental Assessment of Epigenetic Driver Genes
    Downloaded from genome.cshlp.org on October 9, 2021 - Published by Cold Spring Harbor Laboratory Press Resource Pan-cancer multi-omics analysis and orthogonal experimental assessment of epigenetic driver genes Andrea Halaburkova, Vincent Cahais, Alexei Novoloaca, Mariana Gomes da Silva Araujo, Rita Khoueiry,1 Akram Ghantous,1 and Zdenko Herceg1 Epigenetics Group, International Agency for Research on Cancer (IARC), 69008 Lyon, France The recent identification of recurrently mutated epigenetic regulator genes (ERGs) supports their critical role in tumorigen- esis. We conducted a pan-cancer analysis integrating (epi)genome, transcriptome, and DNA methylome alterations in a cu- rated list of 426 ERGs across 33 cancer types, comprising 10,845 tumor and 730 normal tissues. We found that, in addition to mutations, copy number alterations in ERGs were more frequent than previously anticipated and tightly linked to ex- pression aberrations. Novel bioinformatics approaches, integrating the strengths of various driver prediction and multi- omics algorithms, and an orthogonal in vitro screen (CRISPR-Cas9) targeting all ERGs revealed genes with driver roles with- in and across malignancies and shared driver mechanisms operating across multiple cancer types and hallmarks. This is the largest and most comprehensive analysis thus far; it is also the first experimental effort to specifically identify ERG drivers (epidrivers) and characterize their deregulation and functional impact in oncogenic processes. [Supplemental material is available for this article.] Although it has long been known that human cancers harbor both gression, potentially acting as oncogenes or tumor suppressors genetic and epigenetic changes, with an intricate interplay be- (Plass et al. 2013; Vogelstein et al. 2013).
    [Show full text]