Mouse Osbpl8 Conditional Knockout Project (CRISPR/Cas9)
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Redefining the Specificity of Phosphoinositide-Binding by Human
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.20.163253; this version posted June 21, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. Redefining the specificity of phosphoinositide-binding by human PH domain-containing proteins Nilmani Singh1†, Adriana Reyes-Ordoñez1†, Michael A. Compagnone1, Jesus F. Moreno Castillo1, Benjamin J. Leslie2, Taekjip Ha2,3,4,5, Jie Chen1* 1Department of Cell & Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801; 2Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205; 3Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218; 4Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205; 5Howard Hughes Medical Institute, Baltimore, MD 21205, USA †These authors contributed equally to this work. *Correspondence: [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.20.163253; this version posted June 21, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. ABSTRACT Pleckstrin homology (PH) domains are presumed to bind phosphoinositides (PIPs), but specific interaction with and regulation by PIPs for most PH domain-containing proteins are unclear. Here we employed a single-molecule pulldown assay to study interactions of lipid vesicles with full-length proteins in mammalian whole cell lysates. -
Targeting PH Domain Proteins for Cancer Therapy
The Texas Medical Center Library DigitalCommons@TMC The University of Texas MD Anderson Cancer Center UTHealth Graduate School of The University of Texas MD Anderson Cancer Biomedical Sciences Dissertations and Theses Center UTHealth Graduate School of (Open Access) Biomedical Sciences 12-2018 Targeting PH domain proteins for cancer therapy Zhi Tan Follow this and additional works at: https://digitalcommons.library.tmc.edu/utgsbs_dissertations Part of the Bioinformatics Commons, Medicinal Chemistry and Pharmaceutics Commons, Neoplasms Commons, and the Pharmacology Commons Recommended Citation Tan, Zhi, "Targeting PH domain proteins for cancer therapy" (2018). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 910. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/910 This Dissertation (PhD) is brought to you for free and open access by the The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences at DigitalCommons@TMC. It has been accepted for inclusion in The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access) by an authorized administrator of DigitalCommons@TMC. For more information, please contact [email protected]. TARGETING PH DOMAIN PROTEINS FOR CANCER THERAPY by Zhi Tan Approval page APPROVED: _____________________________________________ Advisory Professor, Shuxing Zhang, Ph.D. _____________________________________________ -
Pancreatic Intraductal Tubulopapillary Neoplasm Is Genetically Distinct from Intraductal Papillary Mucinous Neoplasm and Ductal Adenocarcinoma
Modern Pathology (2017) 30, 1760–1772 1760 © 2017 USCAP, Inc All rights reserved 0893-3952/17 $32.00 Pancreatic intraductal tubulopapillary neoplasm is genetically distinct from intraductal papillary mucinous neoplasm and ductal adenocarcinoma Olca Basturk1, Michael F Berger1, Hiroshi Yamaguchi2, Volkan Adsay3, Gokce Askan1, Umesh K Bhanot1, Ahmet Zehir1, Fatima Carneiro4, Seung-Mo Hong5, Giuseppe Zamboni6, Esra Dikoglu7, Vaidehi Jobanputra7,8, Kazimierz O Wrzeszczynski7, Serdar Balci3, Peter Allen9, Naoki Ikari10, Shoko Takeuchi10, Hiroyuki Akagawa10, Atsushi Kanno11, Tooru Shimosegawa11, Takanori Morikawa12, Fuyuhiko Motoi12, Michiaki Unno12, Ryota Higuchi13, Masakazu Yamamoto13, Kyoko Shimizu14, Toru Furukawa15 and David S Klimstra1 1Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; 2Department of Pathology, Tokyo Medical University, Tokyo, Japan; 3Department of Pathology, Emory University, Atlanta, GA, USA; 4Department of Pathology, Centro Hospitalar São João/Faculty of Medicine of Porto University and Institute for Research and Innovation in Health/Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal; 5Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; 6Department of Pathology, University of Verona, Ospedale S.C.-Don Calabria-Negrar, Verona, Italy; 7New York Genome Center, Molecular Diagnostics, New York, NY, USA; 8Department of Pathology, Colombia University Medical Center, New York, NY, USA; 9Department -
Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo -
Supplementary Tables S1-S3
Supplementary Table S1: Real time RT-PCR primers COX-2 Forward 5’- CCACTTCAAGGGAGTCTGGA -3’ Reverse 5’- AAGGGCCCTGGTGTAGTAGG -3’ Wnt5a Forward 5’- TGAATAACCCTGTTCAGATGTCA -3’ Reverse 5’- TGTACTGCATGTGGTCCTGA -3’ Spp1 Forward 5'- GACCCATCTCAGAAGCAGAA -3' Reverse 5'- TTCGTCAGATTCATCCGAGT -3' CUGBP2 Forward 5’- ATGCAACAGCTCAACACTGC -3’ Reverse 5’- CAGCGTTGCCAGATTCTGTA -3’ Supplementary Table S2: Genes synergistically regulated by oncogenic Ras and TGF-β AU-rich probe_id Gene Name Gene Symbol element Fold change RasV12 + TGF-β RasV12 TGF-β 1368519_at serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpine1 ARE 42.22 5.53 75.28 1373000_at sushi-repeat-containing protein, X-linked 2 (predicted) Srpx2 19.24 25.59 73.63 1383486_at Transcribed locus --- ARE 5.93 27.94 52.85 1367581_a_at secreted phosphoprotein 1 Spp1 2.46 19.28 49.76 1368359_a_at VGF nerve growth factor inducible Vgf 3.11 4.61 48.10 1392618_at Transcribed locus --- ARE 3.48 24.30 45.76 1398302_at prolactin-like protein F Prlpf ARE 1.39 3.29 45.23 1392264_s_at serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpine1 ARE 24.92 3.67 40.09 1391022_at laminin, beta 3 Lamb3 2.13 3.31 38.15 1384605_at Transcribed locus --- 2.94 14.57 37.91 1367973_at chemokine (C-C motif) ligand 2 Ccl2 ARE 5.47 17.28 37.90 1369249_at progressive ankylosis homolog (mouse) Ank ARE 3.12 8.33 33.58 1398479_at ryanodine receptor 3 Ryr3 ARE 1.42 9.28 29.65 1371194_at tumor necrosis factor alpha induced protein 6 Tnfaip6 ARE 2.95 7.90 29.24 1386344_at Progressive ankylosis homolog (mouse) -
A Peripheral Blood Gene Expression Signature to Diagnose Subclinical Acute Rejection
CLINICAL RESEARCH www.jasn.org A Peripheral Blood Gene Expression Signature to Diagnose Subclinical Acute Rejection Weijia Zhang,1 Zhengzi Yi,1 Karen L. Keung,2 Huimin Shang,3 Chengguo Wei,1 Paolo Cravedi,1 Zeguo Sun,1 Caixia Xi,1 Christopher Woytovich,1 Samira Farouk,1 Weiqing Huang,1 Khadija Banu,1 Lorenzo Gallon,4 Ciara N. Magee,5 Nader Najafian,5 Milagros Samaniego,6 Arjang Djamali ,7 Stephen I. Alexander,2 Ivy A. Rosales,8 Rex Neal Smith,8 Jenny Xiang,3 Evelyne Lerut,9 Dirk Kuypers,10,11 Maarten Naesens ,10,11 Philip J. O’Connell,2 Robert Colvin,8 Madhav C. Menon,1 and Barbara Murphy1 Due to the number of contributing authors, the affiliations are listed at the end of this article. ABSTRACT Background In kidney transplant recipients, surveillance biopsies can reveal, despite stable graft function, histologic features of acute rejection and borderline changes that are associated with undesirable graft outcomes. Noninvasive biomarkers of subclinical acute rejection are needed to avoid the risks and costs associated with repeated biopsies. Methods We examined subclinical histologic and functional changes in kidney transplant recipients from the prospective Genomics of Chronic Allograft Rejection (GoCAR) study who underwent surveillance biopsies over 2 years, identifying those with subclinical or borderline acute cellular rejection (ACR) at 3 months (ACR-3) post-transplant. We performed RNA sequencing on whole blood collected from 88 indi- viduals at the time of 3-month surveillance biopsy to identify transcripts associated with ACR-3, developed a novel sequencing-based targeted expression assay, and validated this gene signature in an independent cohort. -
Oral Administration of Lactobacillus Plantarum 299V
Genes Nutr (2015) 10:10 DOI 10.1007/s12263-015-0461-7 RESEARCH PAPER Oral administration of Lactobacillus plantarum 299v modulates gene expression in the ileum of pigs: prediction of crosstalk between intestinal immune cells and sub-mucosal adipocytes 1 1,4 1,5 1 Marcel Hulst • Gabriele Gross • Yaping Liu • Arjan Hoekman • 2 1,3 1,3 Theo Niewold • Jan van der Meulen • Mari Smits Received: 19 November 2014 / Accepted: 28 March 2015 / Published online: 11 April 2015 Ó The Author(s) 2015. This article is published with open access at Springerlink.com Abstract To study host–probiotic interactions in parts of ileum. A higher expression level of several B cell-specific the intestine only accessible in humans by surgery (je- transcription factors/regulators was observed, suggesting junum, ileum and colon), pigs were used as model for that an influx of B cells from the periphery to the ileum humans. Groups of eight 6-week-old pigs were repeatedly and/or the proliferation of progenitor B cells to IgA-com- orally administered with 5 9 1012 CFU Lactobacillus mitted plasma cells in the Peyer’s patches of the ileum was plantarum 299v (L. plantarum 299v) or PBS, starting with stimulated. Genes coding for enzymes that metabolize a single dose followed by three consecutive daily dosings leukotriene B4, 1,25-dihydroxyvitamin D3 and steroids 10 days later. Gene expression was assessed with pooled were regulated in the ileum. Bioinformatics analysis pre- RNA samples isolated from jejunum, ileum and colon dicted that these metabolites may play a role in the scrapings of the eight pigs per group using Affymetrix crosstalk between intestinal immune cells and sub-mucosal porcine microarrays. -
Transparent Machine Learning for Multi-Omics Analysis of Mental Disorders
UPTEC X20 015 Examensarbete 30 hp Juni 2020 Transparent Machine Learning for Multi-Omics Analysis of Mental Disorders Stella Belin Abstract Transparent Machine Learning for Multi-Omics Analysis of Mental Disorders Stella Belin Teknisk- naturvetenskaplig fakultet UTH-enheten Schizophrenia and bipolar disorder are two severe mental disorders that affect more than 65 million individuals worldwide. The aim of this Besöksadress: project was to find co-prediction mechanisms for genes associated with Ångströmlaboratoriet Lägerhyddsvägen 1 schizophrenia and bipolar disorder using a multi-omics data set and a Hus 4, Plan 0 transparent machine learning approach. The overall purpose of the project was to further understand the biological mechanisms of these Postadress: complex disorders. In this work, publicly available multi-omics data Box 536 751 21 Uppsala collected from post-mortem brain tissue were used. The omics types included were gene expression, DNA methylation, and SNP array data. The Telefon: data consisted of samples from individuals with schizophrenia, bipolar 018 – 471 30 03 disorder, and healthy controls. Individuals with schizophrenia or Telefax: bipolar disorder were considered as a combined CASE class. 018 – 471 30 00 Using machine learning techniques, a multi-omics pipeline was developed Hemsida: to integrate these data in a manner such that all types were adequately http://www.teknat.uu.se/student represented. A feature selection was performed on methylation and SNP data, where the most important sites were estimated and mapped to their corresponding genes. Next, those genes were intersected with the gene expression data, and another feature selection was performed on the gene expression data. The most important genes were used to develop an interpretable rule-based model with an accuracy of 88%. -
Foraging Shifts and Visual Pre Adaptation in Ecologically Diverse Bats
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/340654059 Foraging shifts and visual preadaptation in ecologically diverse bats Article in Molecular Ecology · April 2020 DOI: 10.1111/mec.15445 CITATIONS READS 0 153 9 authors, including: Kalina T. J. Davies Laurel R Yohe Queen Mary, University of London Yale University 40 PUBLICATIONS 254 CITATIONS 24 PUBLICATIONS 93 CITATIONS SEE PROFILE SEE PROFILE Edgardo M. Rengifo Elizabeth R Dumont University of São Paulo University of California, Merced 13 PUBLICATIONS 28 CITATIONS 115 PUBLICATIONS 3,143 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Ecology of the Greater horseshoe bat View project BAT 1K View project All content following this page was uploaded by Liliana M. Davalos on 14 May 2020. The user has requested enhancement of the downloaded file. Received: 17 October 2019 | Revised: 28 February 2020 | Accepted: 31 March 2020 DOI: 10.1111/mec.15445 ORIGINAL ARTICLE Foraging shifts and visual pre adaptation in ecologically diverse bats Kalina T. J. Davies1 | Laurel R. Yohe2,3 | Jesus Almonte4 | Miluska K. R. Sánchez5 | Edgardo M. Rengifo6,7 | Elizabeth R. Dumont8 | Karen E. Sears9 | Liliana M. Dávalos2,10 | Stephen J. Rossiter1 1School of Biological and Chemical Sciences, Queen Mary University of London, London, UK 2Department of Ecology and Evolution, State University of New York at Stony Brook, Stony Brook, USA 3Department of Geology & Geophysics, Yale University, -
Genome Wide Association of Chronic Kidney Disease Progression: the CRIC Study (Author List and Affiliations Listed at End of Document)
SUPPLEMENTARY MATERIALS Genome Wide Association of Chronic Kidney Disease Progression: The CRIC Study (Author list and affiliations listed at end of document) Genotyping information page 2 Molecular pathway analysis information page 3 Replication cohort acknowledgments page 4 Supplementary Table 1. AA top hit region gene function page 5-6 Supplementary Table 2. EA top hit region gene function page 7 Supplementary Table 3. GSA pathway results page 8 Supplementary Table 4. Number of molecular interaction based on top candidate gene molecular networks page 9 Supplementary Table 5. Results of top gene marker association in AA, based on EA derived candidate gene regions page 10 Supplementary Table 6. Results of top gene marker association in EA, based on AA derived candidate gene regions page 11 Supplementary Table 7. EA Candidate SNP look up page 12 Supplementary Table 8. AA Candidate SNP look up page 13 Supplementary Table 9. Replication cohorts page 14 Supplementary Table 10. Replication cohort study characteristics page 15 Supplementary Figure 1a-b. Boxplot of eGFR decline in AA and EA page 16 Supplementary Figure 2a-l. Regional association plot of candidate SNPs identified in AA groups pages 17-22 Supplementary Figure 3a-f. Regional association plot of candidate SNPs identified in EA groups pages 23-25 Supplementary Figure 4. Molecular Interaction network of candidate genes for renal, cardiovascular and immunological diseases pages 26-27 Supplementary Figure 5. Molecular Interaction network of candidate genes for renal diseases pages 28-29 Supplementary Figure 6. ARRDC4 LD map page 30 Author list and affiliations page 31 1 Supplemental Materials Genotyping Genotyping was performed on a total of 3,635 CRIC participants who provided specific consent for investigations of inherited genetics (of a total of 3,939 CRIC participants). -
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 -
Supplementary Table 1 Double Treatment Vs Single Treatment
Supplementary table 1 Double treatment vs single treatment Probe ID Symbol Gene name P value Fold change TC0500007292.hg.1 NIM1K NIM1 serine/threonine protein kinase 1.05E-04 5.02 HTA2-neg-47424007_st NA NA 3.44E-03 4.11 HTA2-pos-3475282_st NA NA 3.30E-03 3.24 TC0X00007013.hg.1 MPC1L mitochondrial pyruvate carrier 1-like 5.22E-03 3.21 TC0200010447.hg.1 CASP8 caspase 8, apoptosis-related cysteine peptidase 3.54E-03 2.46 TC0400008390.hg.1 LRIT3 leucine-rich repeat, immunoglobulin-like and transmembrane domains 3 1.86E-03 2.41 TC1700011905.hg.1 DNAH17 dynein, axonemal, heavy chain 17 1.81E-04 2.40 TC0600012064.hg.1 GCM1 glial cells missing homolog 1 (Drosophila) 2.81E-03 2.39 TC0100015789.hg.1 POGZ Transcript Identified by AceView, Entrez Gene ID(s) 23126 3.64E-04 2.38 TC1300010039.hg.1 NEK5 NIMA-related kinase 5 3.39E-03 2.36 TC0900008222.hg.1 STX17 syntaxin 17 1.08E-03 2.29 TC1700012355.hg.1 KRBA2 KRAB-A domain containing 2 5.98E-03 2.28 HTA2-neg-47424044_st NA NA 5.94E-03 2.24 HTA2-neg-47424360_st NA NA 2.12E-03 2.22 TC0800010802.hg.1 C8orf89 chromosome 8 open reading frame 89 6.51E-04 2.20 TC1500010745.hg.1 POLR2M polymerase (RNA) II (DNA directed) polypeptide M 5.19E-03 2.20 TC1500007409.hg.1 GCNT3 glucosaminyl (N-acetyl) transferase 3, mucin type 6.48E-03 2.17 TC2200007132.hg.1 RFPL3 ret finger protein-like 3 5.91E-05 2.17 HTA2-neg-47424024_st NA NA 2.45E-03 2.16 TC0200010474.hg.1 KIAA2012 KIAA2012 5.20E-03 2.16 TC1100007216.hg.1 PRRG4 proline rich Gla (G-carboxyglutamic acid) 4 (transmembrane) 7.43E-03 2.15 TC0400012977.hg.1 SH3D19