Mouse Ap3d1 Knockout Project (CRISPR/Cas9)

Total Page:16

File Type:pdf, Size:1020Kb

Mouse Ap3d1 Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Ap3d1 Knockout Project (CRISPR/Cas9) Objective: To create a Ap3d1 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Ap3d1 gene (NCBI Reference Sequence: NM_007460 ; Ensembl: ENSMUSG00000020198 ) is located on Mouse chromosome 10. 31 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 31 (Transcript: ENSMUST00000020420). Exon 2~8 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mutant mice show coat and eye color dilution, platelet defects, lysosomal abnormalities, inner ear degeneration and neurological defects and model Hermansky-Pudlak storage pool deficiency syndrome. Exon 2 starts from about 2.7% of the coding region. Exon 2~8 covers 19.74% of the coding region. The size of effective KO region: ~9414 bp. The KO region does not have any other known gene. Page 1 of 9 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 4 5 6 7 8 31 Legends Exon of mouse Ap3d1 Knockout region Page 2 of 9 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of Exon 2 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 Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 570 bp section downstream of Exon 8 is aligned with itself to determine if there are tandem repeats. Tandem repeats are found in the dot plot matrix. The gRNA site is selected outside of these tandem repeats. Page 3 of 9 https://www.alphaknockout.com Overview of the GC Content Distribution (up) Window size: 300 bp Sequence 12 Summary: Full Length(2000bp) | A(23.0% 460) | C(24.7% 494) | T(27.3% 546) | G(25.0% 500) Note: The 2000 bp section upstream of Exon 2 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. Overview of the GC Content Distribution (down) Window size: 300 bp Sequence 12 Summary: Full Length(570bp) | A(17.72% 101) | C(24.39% 139) | T(32.28% 184) | G(25.61% 146) Note: The 570 bp section downstream of Exon 8 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 4 of 9 https://www.alphaknockout.com BLAT Search Results (up) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 2000 1 2000 2000 100.0% chr10 - 80732947 80734946 2000 browser details YourSeq 162 1022 1207 2000 94.7% chr4 - 155380434 155836588 456155 browser details YourSeq 160 1023 1250 2000 92.6% chr4 - 150456092 150456333 242 browser details YourSeq 156 724 1185 2000 83.8% chr2 - 154766326 154766573 248 browser details YourSeq 154 1022 1212 2000 88.2% chr19 + 28773051 28773236 186 browser details YourSeq 153 1022 1209 2000 91.0% chr4 + 127136807 127137001 195 browser details YourSeq 153 1022 1203 2000 93.3% chr10 + 56240337 56240523 187 browser details YourSeq 151 1024 1209 2000 92.7% chr15 - 100826546 100826735 190 browser details YourSeq 151 1015 1204 2000 87.8% chr19 + 28221071 28221249 179 browser details YourSeq 149 722 1175 2000 81.6% chr11 - 34984720 34984974 255 browser details YourSeq 148 1013 1187 2000 92.6% chr15 - 97771911 97772098 188 browser details YourSeq 147 1023 1209 2000 89.1% chr4 + 58816351 58816535 185 browser details YourSeq 147 1019 1207 2000 92.1% chr2 + 68847023 68847212 190 browser details YourSeq 146 1022 1207 2000 86.7% chr3 - 95158767 95158946 180 browser details YourSeq 146 1022 1209 2000 91.0% chr3 - 69772433 69772622 190 browser details YourSeq 146 1020 1205 2000 89.5% chr2 - 112711854 112712035 182 browser details YourSeq 146 1019 1203 2000 90.7% chr15 - 32775737 32775921 185 browser details YourSeq 146 1015 1206 2000 91.6% chr10 - 51673731 51673924 194 browser details YourSeq 146 1022 1205 2000 90.9% chr2 + 27437225 27437406 182 browser details YourSeq 146 1012 1209 2000 90.2% chr12 + 108676322 108676519 198 Note: The 2000 bp section upstream of Exon 2 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 570 1 570 570 100.0% chr10 - 80722963 80723532 570 browser details YourSeq 40 116 169 570 91.5% chr1 + 21138795 21138855 61 browser details YourSeq 39 97 154 570 76.2% chr10 + 62686762 62686804 43 browser details YourSeq 34 160 226 570 92.5% chr8 - 61706501 61706579 79 browser details YourSeq 34 157 225 570 90.5% chr6 - 95592004 95592490 487 browser details YourSeq 33 105 143 570 88.9% chr10 + 69312781 69312818 38 browser details YourSeq 32 152 209 570 92.2% chr1 + 188880860 188880926 67 browser details YourSeq 30 110 145 570 94.3% chr4 - 88439662 88439718 57 browser details YourSeq 28 205 234 570 96.7% chr7 - 119480375 119480404 30 browser details YourSeq 28 193 223 570 96.7% chr4 - 45250073 45250106 34 browser details YourSeq 28 155 182 570 100.0% chr17 - 87005683 87005710 28 browser details YourSeq 27 156 184 570 96.6% chr14 - 79447205 79447233 29 browser details YourSeq 27 200 238 570 79.0% chr1 - 60108897 60108934 38 browser details YourSeq 25 491 518 570 96.3% chr10 - 22843566 22843601 36 browser details YourSeq 25 155 179 570 100.0% chr14 + 121410791 121410815 25 browser details YourSeq 25 130 155 570 100.0% chr11 + 9215925 9215951 27 browser details YourSeq 25 134 176 570 79.1% chr1 + 131877962 131878004 43 browser details YourSeq 25 137 170 570 96.3% chr1 + 85672961 85672997 37 browser details YourSeq 24 152 175 570 100.0% chr6 - 40421929 40421952 24 browser details YourSeq 24 96 120 570 100.0% chr11 - 35721380 35721405 26 Note: The 570 bp section downstream of Exon 8 is BLAT searched against the genome. No significant similarity is found. Page 5 of 9 https://www.alphaknockout.com Gene and protein information: Ap3d1 adaptor-related protein complex 3, delta 1 subunit [ Mus musculus (house mouse) ] Gene ID: 11776, updated on 12-Aug-2019 Gene summary Official Symbol Ap3d1 provided by MGI Official Full Name adaptor-related protein complex 3, delta 1 subunit provided by MGI Primary source MGI:MGI:107734 See related Ensembl:ENSMUSG00000020198 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 mh; Ap3d; Bolvr; mocha; mBLVR1; AA407035 Expression Ubiquitous expression in testis adult (RPKM 54.8), ovary adult (RPKM 50.0) and 28 other tissues See more Orthologs human all Genomic context Location: 10 C1; 10 39.72 cM See Ap3d1 in Genome Data Viewer Exon count: 32 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 10 NC_000076.6 (80706956..80742303, complement) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 10 NC_000076.5 (80169723..80204956, complement) Chromosome 10 - NC_000076.6 Page 6 of 9 https://www.alphaknockout.com Transcript information: This gene has 7 transcripts Gene: Ap3d1 ENSMUSG00000020198 Description adaptor-related protein complex 3, delta 1 subunit [Source:MGI Symbol;Acc:MGI:107734] Gene Synonyms Bolvr, mBLVR1 Location Chromosome 10: 80,706,956-80,742,264 reverse strand. GRCm38:CM001003.2 About this gene This gene has 7 transcripts (splice variants), 213 orthologues, is a member of 1 Ensembl protein family and is associated with 38 phenotypes. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Ap3d1- ENSMUST00000020420.8 4805 1199aa ENSMUSP00000020420.7 Protein coding CCDS35984 O54774 TSL:1 201 GENCODE basic APPRIS P1 Ap3d1- ENSMUST00000218610.1 786 116aa ENSMUSP00000151820.1 Nonsense mediated - A0A1W2P7Z2 CDS 5' 203 decay incomplete TSL:3 Ap3d1- ENSMUST00000219356.1 766 122aa ENSMUSP00000151355.1 Nonsense mediated - A0A1W2P6Q6 CDS 5' 205 decay incomplete TSL:3 Ap3d1- ENSMUST00000218125.1 3728 No - Retained intron - - TSL:1 202 protein Ap3d1- ENSMUST00000219816.1 858 No - Retained intron - - TSL:2 206 protein Ap3d1- ENSMUST00000219253.1 668 No - Retained intron - - TSL:2 204 protein Ap3d1- ENSMUST00000220183.1 422 No - Retained intron - - TSL:2 207 protein Page 7 of 9 https://www.alphaknockout.com 55.31 kb Forward strand 80.70Mb 80.71Mb 80.72Mb 80.73Mb 80.74Mb 80.75Mb Genes Izumo4-213 >retained intron (Comprehensive set... Izumo4-212 >retained intron Izumo4-202 >retained intron Izumo4-211 >retained intron Izumo4-205 >protein coding Izumo4-201 >protein coding Izumo4-209 >retained intron Izumo4-204 >retained intron Izumo4-203 >retained intron Izumo4-208 >retained intron Izumo4-206 >nonsense mediated decay Izumo4-207 >retained intron Izumo4-210 >retained intron Contigs AC152410.6 > Genes (Comprehensive set... < Mob3a-201protein coding < Ap3d1-201protein coding < Mob3a-202protein coding < Ap3d1-206retained intron < Ap3d1-207retained intron < Mob3a-203lncRNA < Ap3d1-203nonsense mediated decay < Ap3d1-205nonsense mediated decay < Ap3d1-202retained intron < Ap3d1-204retained intron Regulatory Build 80.70Mb 80.71Mb 80.72Mb 80.73Mb 80.74Mb 80.75Mb Reverse strand 55.31 kb Regulation Legend CTCF Enhancer Open Chromatin Promoter Promoter Flank Gene Legend Protein Coding merged Ensembl/Havana Ensembl protein coding Non-Protein Coding processed transcript RNA gene Page 8 of 9 https://www.alphaknockout.com Transcript: ENSMUST00000020420 < Ap3d1-201protein coding Reverse strand 35.31 kb ENSMUSP00000020..
Recommended publications
  • Supplementary Methods
    SUPPLEMENTARY METHODS Epilepsy cohorts Epilepsy cohorts contributing to the meta-analysis are detailed below. EPIGEN (Reported by – Chantal Depondt, Sanjay Sisodiya, Norman Delanty, Gianpiero Cavalleri, Erin Heinzen and David Goldstein) The EPIGEN study consisted of epilepsy cohorts from Beaumont Hospital Dublin (Ireland), Université Libre de Bruxelles (ULB, Belgium), Duke University Medical Centre (North Carolina, USA) and University College Hospital London (UK). Inclusion Criteria: Except for Duke, only adult (>16 years) patients with epilepsy were recruited. Exclusion Criteria: No specific exclusion criteria. Quality assurance: At all sites, subjects were recruited and phenotyped by experienced epilepsy specialists. At Duke, all cases underwent independent case-record review by an epilepsy nurse specialist, and ambiguous diagnoses were re-evaluated by a second epileptologist. If the diagnosis remained unclear, then the patient was excluded from the study. For London, all cases underwent review by independent epileptologists. For Brussels, study PI (Chantal Depondt) reviewed the classification of all cases by case-note review. For Dublin, no systematic quality assurance was undertaken. Site-specific details for each EPIGEN cohort as organized for the analysis are as follows: – EPIGEN-Dublin Patients were recruited from a specialized epilepsy clinic at Beaumont Hospital, Dublin, Ireland. Patients were mostly of Irish ethnicity. Patients were genotyped on the Illumina platform using a combination of chips (610-Quad+550+300v1/Omni1-Quad). – EPIGEN-Brussels Patients were recruited from epilepsy clinics at UZ Gasthuisberg, Katholieke Universiteit Leuven, and Hôpital Erasme, Université Libre de Bruxelles. Patients were largely of Belgian ethnicity. Patients were genotyped on the Illumina platform using a combination of chips (610-Quad/300 V1 & V2).
    [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]
  • Supplementary Figures 1-14 and Supplementary References
    SUPPORTING INFORMATION Spatial Cross-Talk Between Oxidative Stress and DNA Replication in Human Fibroblasts Marko Radulovic,1,2 Noor O Baqader,1 Kai Stoeber,3† and Jasminka Godovac-Zimmermann1* 1Division of Medicine, University College London, Center for Nephrology, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK. 2Insitute of Oncology and Radiology, Pasterova 14, 11000 Belgrade, Serbia 3Research Department of Pathology and UCL Cancer Institute, Rockefeller Building, University College London, University Street, London WC1E 6JJ, UK †Present Address: Shionogi Europe, 33 Kingsway, Holborn, London WC2B 6UF, UK TABLE OF CONTENTS 1. Supplementary Figures 1-14 and Supplementary References. Figure S-1. Network and joint spatial razor plot for 18 enzymes of glycolysis and the pentose phosphate shunt. Figure S-2. Correlation of SILAC ratios between OXS and OAC for proteins assigned to the SAME class. Figure S-3. Overlap matrix (r = 1) for groups of CORUM complexes containing 19 proteins of the 49-set. Figure S-4. Joint spatial razor plots for the Nop56p complex and FIB-associated complex involved in ribosome biogenesis. Figure S-5. Analysis of the response of emerin nuclear envelope complexes to OXS and OAC. Figure S-6. Joint spatial razor plots for the CCT protein folding complex, ATP synthase and V-Type ATPase. Figure S-7. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated by GO to nucleocytoplasmic transport (GO:0006913). Figure S-8. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated to endocytosis (GO:0006897). Figure S-9. Joint spatial razor plots for 401-set proteins annotated by GO to small GTPase mediated signal transduction (GO:0007264) and/or GTPase activity (GO:0003924).
    [Show full text]
  • Genetic and Pharmacological Approaches to Preventing Neurodegeneration
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2012 Genetic and Pharmacological Approaches to Preventing Neurodegeneration Marco Boccitto University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Neuroscience and Neurobiology Commons Recommended Citation Boccitto, Marco, "Genetic and Pharmacological Approaches to Preventing Neurodegeneration" (2012). Publicly Accessible Penn Dissertations. 494. https://repository.upenn.edu/edissertations/494 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/494 For more information, please contact [email protected]. Genetic and Pharmacological Approaches to Preventing Neurodegeneration Abstract The Insulin/Insulin-like Growth Factor 1 Signaling (IIS) pathway was first identified as a major modifier of aging in C.elegans. It has since become clear that the ability of this pathway to modify aging is phylogenetically conserved. Aging is a major risk factor for a variety of neurodegenerative diseases including the motor neuron disease, Amyotrophic Lateral Sclerosis (ALS). This raises the possibility that the IIS pathway might have therapeutic potential to modify the disease progression of ALS. In a C. elegans model of ALS we found that decreased IIS had a beneficial effect on ALS pathology in this model. This beneficial effect was dependent on activation of the transcription factor daf-16. To further validate IIS as a potential therapeutic target for treatment of ALS, manipulations of IIS in mammalian cells were investigated for neuroprotective activity. Genetic manipulations that increase the activity of the mammalian ortholog of daf-16, FOXO3, were found to be neuroprotective in a series of in vitro models of ALS toxicity.
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • Pct.1 Pct.2 Adj P
    Supplementary material Gut Supplementary Table 3. Marker genes in each malignant epithelial cells cluster Gene Log(Fold-change) Pct.1 Pct.2 Adj p- value Cluster LEFTY1 2.690865644 0.899 0.067 0.00E+00 C1 TFF2 2.186713379 0.855 0.36 0.00E+00 C1 CCT2 1.701377329 0.801 0.283 0.00E+00 C1 SPINK4 1.663911876 0.654 0.128 0.00E+00 C1 TFF3 1.632604627 0.882 0.43 0.00E+00 C1 CXCL17 1.616238928 0.659 0.079 0.00E+00 C1 LDHB 1.407263152 0.711 0.137 0.00E+00 C1 CLDN18 1.398880496 0.825 0.278 0.00E+00 C1 TESC 1.382790712 0.79 0.212 0.00E+00 C1 PDIA6 1.295630983 0.789 0.404 0.00E+00 C1 PSCA 1.263596359 0.922 0.241 0.00E+00 C1 SRD5A3 1.246561606 0.586 0.098 0.00E+00 C1 PDIA4 1.180717046 0.849 0.419 0.00E+00 C1 DPCR1 1.175754587 0.506 0.06 0.00E+00 C1 WBP5 1.163957541 0.734 0.106 0.00E+00 C1 LYZ 1.141614179 0.969 0.727 0.00E+00 C1 RAP1B 1.125989315 0.68 0.255 0.00E+00 C1 S100P 1.055862172 0.962 0.758 0.00E+00 C1 OS9 1.05478066 0.787 0.338 0.00E+00 C1 R3HDM2 1.031695733 0.742 0.167 0.00E+00 C1 B3GNT7 1.031632795 0.549 0.088 0.00E+00 C1 MORF4L1 1.023176967 0.768 0.409 0.00E+00 C1 TMEM165 0.999912261 0.649 0.196 0.00E+00 C1 GALNT3 0.995165397 0.705 0.254 0.00E+00 C1 TFF1 0.962929536 0.96 0.591 0.00E+00 C1 LY6D 0.94328396 0.484 0.007 0.00E+00 C1 MIA 0.895355862 0.623 0.103 0.00E+00 C1 MDM2 0.864864363 0.615 0.089 0.00E+00 C1 YEATS4 0.864197638 0.585 0.128 0.00E+00 C1 CPM 0.831257805 0.511 0.026 0.00E+00 C1 NGFRAP1 0.776686405 0.552 0.027 0.00E+00 C1 TCEAL8 0.747773737 0.549 0.064 0.00E+00 C1 SFTA2 0.710176136 0.52 0.066 0.00E+00 C1 FKBP10 0.567908872 0.427
    [Show full text]
  • Post-Transcriptionally Impaired De Novo Mutations Contribute to The
    bioRxiv preprint doi: https://doi.org/10.1101/175844; this version posted November 26, 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 Post-transcriptionally impaired de novo mutations 2 contribute to the genetic etiology of four neuropsychiatric 3 disorders 4 5 Fengbiao Mao1,2¶, Lu Wang3¶, Xiaolu Zhao2, Zhongshan Li4, Luoyuan Xiao5, 6 Rajesh C. Rao2, Jinchen Li4, Huajing Teng1*, Xin He6*, and Zhong Sheng Sun1,4* 7 8 1 Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, 9 China. 10 2 Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA. 11 3 Institute of Life Science, Southeast University, Nanjing 210096, China. 12 4 Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou 325027, 13 China 14 5 Department of Computer Science and Technology, Tsinghua University, Beijing 15 100084, China. 16 6 Department of Human Genetics, University of Chicago, Chicago, IL, USA. 17 18 ¶These authors contributed equally to this work 19 * Corresponding authors 20 E-mail: 21 [email protected] (Z.S.S.) 22 [email protected] (X.H.) 23 [email protected] (H.T.) 24 25 1 bioRxiv preprint doi: https://doi.org/10.1101/175844; this version posted November 26, 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.
    [Show full text]
  • A Trafficome-Wide Rnai Screen Reveals Deployment of Early and Late Secretory Host Proteins and the Entire Late Endo-/Lysosomal V
    bioRxiv preprint doi: https://doi.org/10.1101/848549; this version posted November 19, 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 4.0 International license. 1 A trafficome-wide RNAi screen reveals deployment of early and late 2 secretory host proteins and the entire late endo-/lysosomal vesicle fusion 3 machinery by intracellular Salmonella 4 5 Alexander Kehl1,4, Vera Göser1, Tatjana Reuter1, Viktoria Liss1, Maximilian Franke1, 6 Christopher John1, Christian P. Richter2, Jörg Deiwick1 and Michael Hensel1, 7 8 1Division of Microbiology, University of Osnabrück, Osnabrück, Germany; 2Division of Biophysics, University 9 of Osnabrück, Osnabrück, Germany, 3CellNanOs – Center for Cellular Nanoanalytics, Fachbereich 10 Biologie/Chemie, Universität Osnabrück, Osnabrück, Germany; 4current address: Institute for Hygiene, 11 University of Münster, Münster, Germany 12 13 Running title: Host factors for SIF formation 14 Keywords: siRNA knockdown, live cell imaging, Salmonella-containing vacuole, Salmonella- 15 induced filaments 16 17 Address for correspondence: 18 Alexander Kehl 19 Institute for Hygiene 20 University of Münster 21 Robert-Koch-Str. 4148149 Münster, Germany 22 Tel.: +49(0)251/83-55233 23 E-mail: [email protected] 24 25 or bioRxiv preprint doi: https://doi.org/10.1101/848549; this version posted November 19, 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 4.0 International license.
    [Show full text]
  • CD4+ T Cells from Children with Active Juvenile Idiopathic Arthritis Show
    www.nature.com/scientificreports OPEN CD4+ T cells from children with active juvenile idiopathic arthritis show altered chromatin features associated with transcriptional abnormalities Evan Tarbell1,3,5,7, Kaiyu Jiang2,7, Teresa R. Hennon2, Lucy Holmes2, Sonja Williams2, Yao Fu4, Patrick M. Gafney4, Tao Liu1,3,6 & James N. Jarvis2,3* Juvenile idiopathic arthritis (JIA) is one of the most common chronic diseases in children. While clinical outcomes for patients with juvenile JIA have improved, the underlying biology of the disease and mechanisms underlying therapeutic response/non-response are poorly understood. We have shown that active JIA is associated with distinct transcriptional abnormalities, and that the attainment of remission is associated with reorganization of transcriptional networks. In this study, we used a multi- omics approach to identify mechanisms driving the transcriptional abnormalities in peripheral blood CD4+ T cells of children with active JIA. We demonstrate that active JIA is associated with alterations in CD4+ T cell chromatin, as assessed by ATACseq studies. However, 3D chromatin architecture, assessed by HiChIP and simultaneous mapping of CTCF anchors of chromatin loops, reveals that normal 3D chromatin architecture is largely preserved. Overlapping CTCF binding, ATACseq, and RNAseq data with known JIA genetic risk loci demonstrated the presence of genetic infuences on the observed transcriptional abnormalities and identifed candidate target genes. These studies demonstrate the utility of multi-omics approaches for unraveling important questions regarding the pathobiology of autoimmune diseases. Juvenile idiopathic arthritis (JIA) is a broad term that describes a clinically heterogeneous group of diseases characterized by chronic synovial hypertrophy and infammation, with onset before 16 years of age 1.
    [Show full text]
  • Golgipathies in Neurodevelopment: a New View of Old Defects Sowmyalakshmi Rasika, Sandrine Passemard, Alain Verloes, Pierre Gressens, Vincent El Ghouzzi
    Golgipathies in Neurodevelopment: A New View of Old Defects Sowmyalakshmi Rasika, Sandrine Passemard, Alain Verloes, Pierre Gressens, Vincent El ghouzzi To cite this version: Sowmyalakshmi Rasika, Sandrine Passemard, Alain Verloes, Pierre Gressens, Vincent El ghouzzi. Golgipathies in Neurodevelopment: A New View of Old Defects. Developmental Neuroscience, Karger, 2019, 40 (5-6), pp.396-416. 10.1159/000497035. hal-02322665 HAL Id: hal-02322665 https://hal.archives-ouvertes.fr/hal-02322665 Submitted on 2 Jun 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Rasika et al, Golgipathies and Neurodevelopment 1 2 Golgipathies in Neurodevelopment: 3 A New View of Old Defects 4 5 1,2 1,2 1,2 6 Sowmyalakshmi Rasika , Sandrine Passemard , Alain Verloes , Pierre 1,3 1* 7 Gressens , Vincent El Ghouzzi 8 9 1. PROTECT, INSERM, Université Paris Diderot, Sorbonne Paris Cité, Paris, France 10 2. AP HP, Hôpital Robert Debré, UF de Génétique Clinique, Paris, France 11 3. Centre for the Developing Brain, Division of Imaging Sciences and Biomedical 12 Engineering, King’s College London, King’s Health Partners, St. Thomas’ Hospital, 13 London, United Kingdom 14 15 *Corresponding author: 16 Vincent El Ghouzzi 17 Address: Inserm U1141, Hôpital Robert-Debré, 48 Boulevard Sérurier, F-75019, 18 Paris, France.
    [Show full text]
  • Spatial Sorting Enables Comprehensive Characterization of Liver Zonation
    ARTICLES https://doi.org/10.1038/s42255-019-0109-9 Spatial sorting enables comprehensive characterization of liver zonation Shani Ben-Moshe1,3, Yonatan Shapira1,3, Andreas E. Moor 1,2, Rita Manco1, Tamar Veg1, Keren Bahar Halpern1 and Shalev Itzkovitz 1* The mammalian liver is composed of repeating hexagonal units termed lobules. Spatially resolved single-cell transcriptomics has revealed that about half of hepatocyte genes are differentially expressed across the lobule, yet technical limitations have impeded reconstructing similar global spatial maps of other hepatocyte features. Here, we show how zonated surface markers can be used to sort hepatocytes from defined lobule zones with high spatial resolution. We apply transcriptomics, microRNA (miRNA) array measurements and mass spectrometry proteomics to reconstruct spatial atlases of multiple zon- ated features. We demonstrate that protein zonation largely overlaps with messenger RNA zonation, with the periportal HNF4α as an exception. We identify zonation of miRNAs, such as miR-122, and inverse zonation of miRNAs and their hepa- tocyte target genes, highlighting potential regulation of gene expression levels through zonated mRNA degradation. Among the targets, we find the pericentral Wingless-related integration site (Wnt) receptors Fzd7 and Fzd8 and the periportal Wnt inhibitors Tcf7l1 and Ctnnbip1. Our approach facilitates reconstructing spatial atlases of multiple cellular features in the liver and other structured tissues. he mammalian liver is a structured organ, consisting of measurements would broaden our understanding of the regulation repeating hexagonally shaped units termed ‘lobules’ (Fig. 1a). of liver zonation and could be used to model liver metabolic func- In mice, each lobule consists of around 9–12 concentric lay- tion more precisely.
    [Show full text]
  • Identification of Genetic Modifiers of TDP-43: Inflammatory Activation of Astrocytes for Neuroinflammation
    cells Article Identification of Genetic Modifiers of TDP-43: Inflammatory Activation of Astrocytes for Neuroinflammation Jae-Hong Kim 1,2,†, Md Habibur Rahman 1,2,3,†, Donghwi Park 1,‡, Myungjin Jo 1,§, Hyung-Jun Kim 4 and Kyoungho Suk 1,2,3,* 1 Department of Pharmacology, School of Medicine, Kyungpook National University, Daegu 41944, Korea; [email protected] (J.-H.K.); [email protected] (M.H.R.); [email protected] (D.P.); [email protected] (M.J.) 2 BK21 Plus KNU Biomedical Convergence Program, Department of Biomedical Sciences, School of Medicine, Kyungpook National University, Daegu 412944, Korea 3 Brain Science & Engineering Institute, Kyungpook National University, Daegu 41566, Korea 4 Dementia Research Group, Korea Brain Research Institute (KBRI), Daegu 41062, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-53-420-4835 † These authors contributed equally to this work. ‡ Current address: Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Korea. § Current address: Korea Brain Research Institute, Daegu 41062, Korea. Abstract: Transactive response DNA-binding protein 43 (TDP-43) is a ubiquitously expressed DNA/RNA-binding protein linked to amyotrophic lateral sclerosis (ALS) and frontotemporal demen- tia (FTD). TDP-43 has been implicated in numerous aspects of the mRNA life cycle, as well as in cell toxicity and neuroinflammation. In this study, we used the toxicity of the TDP-43 expression in Sac- charomyces cerevisiae as an assay to identify TDP-43 genetic interactions. Specifically, we transformed human TDP-43 cDNAs of wild-type or disease-associated mutants (M337V and Q331K) en masse into Citation: Kim, J.-H.; Rahman, M.H.; Park, D.; Jo, M.; Kim, H.-J.; Suk, K.
    [Show full text]