An Evolutionary Medicine Perspective on Neandertal Extinction

Total Page:16

File Type:pdf, Size:1020Kb

An Evolutionary Medicine Perspective on Neandertal Extinction Supplementary Information (Figures and Tables) for: An evolutionary medicine perspective on Neandertal extinction Alexis P. Sullivan1, Marc de Manuel3, Tomas Marques-Bonet3,4,5, & George H. Perry1,2 Departments of 1Biology and 2Anthropology, Pennsylvania State University, University Park, PA 16802, USA 3Institut de Biologia Evolutiva (CSIC/UPF), Parque de Investigación Biomédica de Barcelona (PRBB), Barcelona, Catalonia 08003, Spain 4CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain 5Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain Corresponding Author: George H. Perry E-mail: [email protected] Supplemental Figure 1: Innate immune system gene permutation analyses – 10,000 sets of 73 randomly selected genes containing nonsynonymous SNPs Supplemental Figure 2: Virus-interacting protein gene permutation analyses – 10,000 sets of 164 randomly selected genes containing nonsynonymous SNPs Supplemental Figure 3: MHC gene permutation analyses – 10,000 sets of 13 randomly selected genes containing nonsynonymous SNPs Supplemental Figure 4: Patterns of Neandertal and modern human nonsynonymous SNP diversity in MHC genes (n = 13) excluding the Altai Neandertal and one random modern human per population Supplemental Figure 5: Significantly enriched gene ontology categories (red) among top 1% ape diversity genes Supplemental Table 1: A comparison of genome-wide nonsynonymous SNPs versus total (nonsynonymous + synonymous) SNPs between Neandertal and modern human populations Supplemental Table 2: PolyPhen-2 predictions for genome-wide nonsynonymous SNPs – damaging versus not damaging – for Neandertal and modern human populations Supplemental Table 3: List of Innate immune system genes Supplemental Table 4: List of virus-interacting protein genes with known antiviral or broader immune activities Supplemental Table 5: List of MHC genes Supplemental Table 6: A comparison of nonsynonymous SNPs (benign + damaging) in innate immune system genes versus genome-wide genes (not including innate immune genes) between Neandertal and modern human populations Supplemental Table 7: A comparison of nonsynonymous SNPs (benign + damaging) in virus- interacting protein genes versus genome-wide genes (not including virus-interacting protein genes) between Neandertal and modern human populations Supplemental Table 8: A comparison of nonsynonymous SNPs (benign + damaging) in MHC genes versus genome-wide genes (not including MHC genes) between Neandertal and modern human populations Supplemental Table 9: Neandertal Minor Allele Frequency (MAF) comparison of nonsynonymous SNPs in MHC genes Supplemental Table 10: A comparison of nonsynonymous SNP (benign + damaging) Minor Allele Frequencies (MAFs) in MHC genes between Neandertal and modern human populations Supplemental Table 11: List of top 1% ape diversity genes Supplemental Table 12: Results of the top 1% ape diversity genes Gene Ontology enrichment analysis Supplemental Table 13: A comparison of nonsynonymous SNPs (benign + damaging) in top 1% ape diversity genes between Neandertal and modern human populations Supplemental Figure 1: Innate immune system gene permutation analyses - 10,000 sets of 73 randomly selected genes containing nonsynonymous SNPs Neandertal-European 800 Observed value 600 400 Probability that the observed ratio is 200 less than that expected by chance: Frequency of permutations P = 0.1944 0 0 0.5 1.0 1.5 2.0 2.5 Ratio Neandertal:European human nonsynonymous SNPs Neandertal-African Neandertal-Asian 800 800 Observed value Observed value 600 600 400 400 Probability that the Probability that the observed ratio is observed ratio is 200 200 less than that less than that expected by chance: expected by chance: Frequency of permutations P = 0.2789 Frequency of permutations P = 0.1400 0 0 0 0.5 1.0 1.5 2.0 2.5 0 0.5 1.0 1.5 2.0 2.5 Ratio Neandertal:African human Ratio Neandertal:Asian human nonsynonymous SNPs nonsynonymous SNPs Supplemental Figure 2: Virus-interacting protein gene permutation analyses - 10,000 sets of 164 randomly selected genes containing nonsynonymous SNPs Neandertal-European 700 Observed value 600 500 400 300 Probability that the observed ratio is 200 greater than that expected by chance: 100 Frequency of permutations P = 0.3547 0 0 0.5 1.0 1.5 Ratio Neandertal:European human nonsynonymous SNPs Neandertal-African Neandertal-Asian 700 700 Observed value Observed value 600 600 500 500 400 400 300 300 Probability that the Probability that the 200 observed ratio is 200 observed ratio is greater than that greater than that expected by chance: expected by chance: 100 100 Frequency of permutations P = 0.0647 Frequency of permutations P = 0.2618 0 0 0 0.5 1.0 1.5 0 0.5 1.0 1.5 Ratio Neandertal:African human Ratio Neandertal:Asian human nonsynonymous SNPs nonsynonymous SNPs Supplemental Figure 3: MHC gene permutation analyses - 10,000 sets of 13 randomly selected genes containing nonsynonymous SNPs Neandertal-European 2000 Observed value 1500 1000 Probability that the observed ratio is 500 greater than that expected by chance: Frequency of permutations P = 0.1556 0 0 2.0 4.0 6.0 8.0 Ratio Neandertal:European human nonsynonymous SNPs Neandertal-African Neandertal-Asian 2000 2000 Observed value Observed value 1500 1500 1000 1000 Probability that the Probability that the observed ratio is observed ratio is 500 greater than that 500 greater than that expected by chance: expected by chance: Frequency of permutations P = 0.0222 Frequency of permutations P = 0.0759 0 0 0 2.0 4.0 6.0 8.0 0 2.0 4.0 6.0 8.0 Ratio Neandertal:African human Ratio Neandertal:Asian human nonsynonymous SNPs nonsynonymous SNPs Supplemental Figure 4: Patterns of Neandertal and modern human nonsynonymous SNP diversity in MHC genes (n = 13) excluding the Altai Neandertal and one random modern human per popula- tion A 20 Predicted damaging 15 Predicted benign 10 5 Number of nonsyn. SNPs 0 Neandertal African European Asian Population B 1.0 MAF = 2 0.8 MAF = 1 0.6 0.4 0.2 Proportion of nonsyn. SNPs 0.0 Neandertal African European Asian Population Supplemental Figure 5: Significantly enriched gene ontology categories (red) among top 1% ape diversity genes Supplemental Table 1: A comparison of genome-wide nonsynonymous SNPs versus total (nonsynonymous + synonymous) SNPs between Neandertal and modern human populations Population Neandertal African European Asian Total Nonsynonymous SNPs 2469 6445 4561 4654 Total Synonymous SNPs 2472 8195 5692 5652 Total Nonsyn/Synon SNPs 4941 14640 10253 10306 Proportion Nonsyn:Total SNPs 0.4997 0.4402 0.4448 0.4516 Proportion Neandertal Nonsyn. - 0.383 0.541 0.531 SNPs to Modern Human Population Fisher's Exact Test P-value - 4.52E-13 2.29E-10 2.59E-08 Supplemental Table 2: PolyPhen-2 predictions for genome-wide nonsynonymous SNPs - damaging versus not damaging - for Neandertal and modern human populations Population Neandertal African European Asian Total Damaging Nonsyn. SNPs 1073 1878 1331 1380 Total Not-Damaging Nonsyn. SNPs 1396 4567 3230 3274 Total Nonsynonymous SNPs 2469 6445 4561 4654 Proportion Damaging SNPs:Total Nonsyn. SNPs 0.4346 0.2914 0.2918 0.2965 Fisher's Exact Test P-value - 2.20E-16 2.20E-16 2.20E-16 Supplemental Table 3: List of innate immune system genes Gene ID Description NLRP1 NOD-like receptors NLRP2 NOD-like receptors NLRP3 NOD-like receptors *See note NLRP4 NOD-like receptors NLRP5 NOD-like receptors NLRP6 NOD-like receptors NLRP7 NOD-like receptors NLRP8 NOD-like receptors NLRP9 NOD-like receptors NLRP10 NOD-like receptors NLRP11 NOD-like receptors NLRP12 NOD-like receptors NLRP13 NOD-like receptors NLRP14 NOD-like receptors NOD1 NOD-like receptors NOD2 NOD-like receptors NLRC3 NOD-like receptors NLRC4 NOD-like receptors NLRC5 NOD-like receptors *See note NLRX1 NOD-like receptors *See note CIITA NOD-like receptors *See note NAIP NOD-like receptors RIG-1 RIG-I-like receptors IFIH1 RIG-I-like receptors *See note LGP2 RIG-I-like receptors TLR1 Toll-Like receptors TLR2 Toll-Like receptors *See note TLR3 Toll-Like receptors *See note TLR4 Toll-Like receptors *See note TLR5 Toll-Like receptors TLR6 Toll-Like receptors TLR7 Toll-Like receptors TLR8 Toll-Like receptors TLR9 Toll-Like receptors TLR10 Toll-Like receptors CLEC7A C-type lectins CD209 C-type lectins CARD9 C-type lectins CLECL1 C-type lectins MR C-type lectins CD206 C-type lectins MRC1 C-type lectins CLEC16A C-type lectins IFI16 Cytosololic DNA sensors MNDA Cytosololic DNA sensors IFIX Cytosololic DNA sensors AIM2 Cytosololic DNA sensors MYD88 adaptors TRIF adaptors MAL adaptors TRAM adaptors IRAK4 adaptors IRAK1 adaptors C3 alternative pathway;classical pathways, Lectin pathway *See note C5 alternative pathway;classical pathways, Lectin pathway C6 alternative pathway;classical pathways, Lectin pathway C7 alternative pathway;classical pathways, Lectin pathway C8A alternative pathway;classical pathways, Lectin pathway C9 alternative pathway;classical pathways, Lectin pathway *See note CFB alternative pathway CFD alternative pathway CFP alternative pathway C2 classical pathways, Lectin pathway C1QA classical pathway C1QB classical pathway C1QC classical pathway C1R classical pathway *See note C1S classical pathway C4A classical pathways, Lectin pathway C4B classical pathways, Lectin pathway MASP1 Lectin pathway MASP2 Lectin pathway MBL2 Lectin pathway *Overlap with virus-interacting protein gene list Supplemental Table 4: List of virus-interacting protein
Recommended publications
  • Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
    bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity.
    [Show full text]
  • Database Tool the Systematic Annotation of the Three Main GPCR
    Database, Vol. 2010, Article ID baq018, doi:10.1093/database/baq018 ............................................................................................................................................................................................................................................................................................. Database tool The systematic annotation of the three main Downloaded from https://academic.oup.com/database/article-abstract/doi/10.1093/database/baq018/406672 by guest on 15 January 2019 GPCR families in Reactome Bijay Jassal1, Steven Jupe1, Michael Caudy2, Ewan Birney1, Lincoln Stein2, Henning Hermjakob1 and Peter D’Eustachio3,* 1European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK, 2Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada and 3New York University School of Medicine, New York, NY 10016, USA *Corresponding author: Tel: +212 263 5779; Fax: +212 263 8166; Email: [email protected] Submitted 14 April 2010; Revised 14 June 2010; Accepted 13 July 2010 ............................................................................................................................................................................................................................................................................................. Reactome is an open-source, freely available database of human biological pathways and processes. A major goal of our work is to provide an integrated view of cellular signalling processes that spans from ligand–receptor
    [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]
  • Mathematical Modeling of Noise and Discovery of Genetic Expression Classes in Gliomas
    Oncogene (2002) 21, 7164 – 7174 ª 2002 Nature Publishing Group All rights reserved 0950 – 9232/02 $25.00 www.nature.com/onc Mathematical modeling of noise and discovery of genetic expression classes in gliomas Hassan M Fathallah-Shaykh*,1, Mo Rigen1, Li-Juan Zhao1, Kanti Bansal1, Bin He1, Herbert H Engelhard3, Leonard Cerullo2, Kelvin Von Roenn2, Richard Byrne2, Lorenzo Munoz2, Gail L Rosseau2, Roberta Glick4, Terry Lichtor4 and Elia DiSavino1 1Department of Neurological Sciences, Rush Presbyterian – St. Lukes Medical Center, Chicago, Illinois, IL 60612, USA; 2Department of Neurosurgery, Rush Presbyterian – St. Lukes Medical Center, Chicago, Illinois, IL 60612, USA; 3Department of Neurosurgery, The University of Illinois at Chicago, Chicago, Illinois, IL 60612, USA; 4Department of Neurosurgery, The Cook County Hospital, Chicago, Illinois, IL 60612, USA The microarray array experimental system generates genetic repertoire in any disease-affected tissue. noisy data that require validation by other experimental However, genome-wide screening is still hampered by methods for measuring gene expression. Here we present the preponderance of false positive data in the gene an algebraic modeling of noise that extracts expression microarray experimental system (Ting Lee et al., 2000). measurements true to a high degree of confidence. This The following experiments are designed to profile the work profiles the expression of 19 200 cDNAs in 35 expression of 19 200 cDNAs in 35 human glioma human gliomas; the experiments are designed to generate samples. Here, we apply mathematical principles to four replicate spots/gene with switching of probes. The separate the noise and extract genes whose expression validity of the extracted measurements is confirmed by: levels are considered truly changed, to a high degree of (1) cluster analysis that generates a molecular classifica- confidence, in the tumor samples as compared to tion differentiating glioblastoma from lower-grade tumors normal brain.
    [Show full text]
  • Transcriptomic Analysis of Native Versus Cultured Human and Mouse Dorsal Root Ganglia Focused on Pharmacological Targets Short
    bioRxiv preprint doi: https://doi.org/10.1101/766865; this version posted September 12, 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-ND 4.0 International license. Transcriptomic analysis of native versus cultured human and mouse dorsal root ganglia focused on pharmacological targets Short title: Comparative transcriptomics of acutely dissected versus cultured DRGs Andi Wangzhou1, Lisa A. McIlvried2, Candler Paige1, Paulino Barragan-Iglesias1, Carolyn A. Guzman1, Gregory Dussor1, Pradipta R. Ray1,#, Robert W. Gereau IV2, # and Theodore J. Price1, # 1The University of Texas at Dallas, School of Behavioral and Brain Sciences and Center for Advanced Pain Studies, 800 W Campbell Rd. Richardson, TX, 75080, USA 2Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine # corresponding authors [email protected], [email protected] and [email protected] Funding: NIH grants T32DA007261 (LM); NS065926 and NS102161 (TJP); NS106953 and NS042595 (RWG). The authors declare no conflicts of interest Author Contributions Conceived of the Project: PRR, RWG IV and TJP Performed Experiments: AW, LAM, CP, PB-I Supervised Experiments: GD, RWG IV, TJP Analyzed Data: AW, LAM, CP, CAG, PRR Supervised Bioinformatics Analysis: PRR Drew Figures: AW, PRR Wrote and Edited Manuscript: AW, LAM, CP, GD, PRR, RWG IV, TJP All authors approved the final version of the manuscript. 1 bioRxiv preprint doi: https://doi.org/10.1101/766865; this version posted September 12, 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]
  • Functional Parsing of Driver Mutations in the Colorectal Cancer Genome Reveals Numerous Suppressors of Anchorage-Independent
    Supplementary information Functional parsing of driver mutations in the colorectal cancer genome reveals numerous suppressors of anchorage-independent growth Ugur Eskiocak1, Sang Bum Kim1, Peter Ly1, Andres I. Roig1, Sebastian Biglione1, Kakajan Komurov2, Crystal Cornelius1, Woodring E. Wright1, Michael A. White1, and Jerry W. Shay1. 1Department of Cell Biology, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390-9039. 2Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77054. Supplementary Figure S1. K-rasV12 expressing cells are resistant to p53 induced apoptosis. Whole-cell extracts from immortalized K-rasV12 or p53 down regulated HCECs were immunoblotted with p53 and its down-stream effectors after 10 Gy gamma-radiation. ! Supplementary Figure S2. Quantitative validation of selected shRNAs for their ability to enhance soft-agar growth of immortalized shTP53 expressing HCECs. Each bar represents 8 data points (quadruplicates from two separate experiments). Arrows denote shRNAs that failed to enhance anchorage-independent growth in a statistically significant manner. Enhancement for all other shRNAs were significant (two tailed Studentʼs t-test, compared to none, mean ± s.e.m., P<0.05)." ! Supplementary Figure S3. Ability of shRNAs to knockdown expression was demonstrated by A, immunoblotting for K-ras or B-E, Quantitative RT-PCR for ERICH1, PTPRU, SLC22A15 and SLC44A4 48 hours after transfection into 293FT cells. Two out of 23 tested shRNAs did not provide any knockdown. " ! Supplementary Figure S4. shRNAs against A, PTEN and B, NF1 do not enhance soft agar growth in HCECs without oncogenic manipulations (Student!s t-test, compared to none, mean ± s.e.m., ns= non-significant).
    [Show full text]
  • Human Artificial Chromosome (Hac) Vector
    Europäisches Patentamt *EP001559782A1* (19) European Patent Office Office européen des brevets (11) EP 1 559 782 A1 (12) EUROPEAN PATENT APPLICATION published in accordance with Art. 158(3) EPC (43) Date of publication: (51) Int Cl.7: C12N 15/09, C12N 1/15, 03.08.2005 Bulletin 2005/31 C12N 1/19, C12N 1/21, C12N 5/10, C12P 21/02 (21) Application number: 03751334.8 (86) International application number: (22) Date of filing: 03.10.2003 PCT/JP2003/012734 (87) International publication number: WO 2004/031385 (15.04.2004 Gazette 2004/16) (84) Designated Contracting States: • KATOH, Motonobu, Tottori University AT BE BG CH CY CZ DE DK EE ES FI FR GB GR Yonago-shi, Tottori 683-8503 (JP) HU IE IT LI LU MC NL PT RO SE SI SK TR • TOMIZUKA, Kazuma, Designated Extension States: Kirin Beer Kabushiki Kaisha AL LT LV MK Takashi-shi, Gunma 370-1295 (JP) • KUROIWA, Yoshimi, (30) Priority: 04.10.2002 JP 2002292853 Kirin Beer Kabushiki Kaisha Takasaki-shi, Gunma 370-1295 (JP) (71) Applicant: KIRIN BEER KABUSHIKI KAISHA • KAKEDA, Minoru, Kirin Beer Kabushiki Kaisha Tokyo 104-8288 (JP) Takasaki-shi, Gunma 370-1295 (JP) (72) Inventors: (74) Representative: HOFFMANN - EITLE • OSHIMURA, Mitsuo, Tottori University Patent- und Rechtsanwälte Yonago-shi, Tottori 683-8503 (JP) Arabellastrasse 4 81925 München (DE) (54) HUMAN ARTIFICIAL CHROMOSOME (HAC) VECTOR (57) The present invention relates to a human arti- ing a cell which expresses foreign DNA. Furthermore, ficial chromosome (HAC) vector and a method for pro- the present invention relates to a method for producing ducing the same.
    [Show full text]
  • 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).
    [Show full text]
  • Copy Number Variation in Fetal Alcohol Spectrum Disorder
    Biochemistry and Cell Biology Copy number variation in fetal alcohol spectrum disorder Journal: Biochemistry and Cell Biology Manuscript ID bcb-2017-0241.R1 Manuscript Type: Article Date Submitted by the Author: 09-Nov-2017 Complete List of Authors: Zarrei, Mehdi; The Centre for Applied Genomics Hicks, Geoffrey G.; University of Manitoba College of Medicine, Regenerative Medicine Reynolds, James N.; Queen's University School of Medicine, Biomedical and Molecular SciencesDraft Thiruvahindrapuram, Bhooma; The Centre for Applied Genomics Engchuan, Worrawat; Hospital for Sick Children SickKids Learning Institute Pind, Molly; University of Manitoba College of Medicine, Regenerative Medicine Lamoureux, Sylvia; The Centre for Applied Genomics Wei, John; The Centre for Applied Genomics Wang, Zhouzhi; The Centre for Applied Genomics Marshall, Christian R.; The Centre for Applied Genomics Wintle, Richard; The Centre for Applied Genomics Chudley, Albert; University of Manitoba Scherer, Stephen W.; The Centre for Applied Genomics Is the invited manuscript for consideration in a Special Fetal Alcohol Spectrum Disorder Issue? : Keyword: Fetal alcohol spectrum disorder, FASD, copy number variations, CNV https://mc06.manuscriptcentral.com/bcb-pubs Page 1 of 354 Biochemistry and Cell Biology 1 Copy number variation in fetal alcohol spectrum disorder 2 Mehdi Zarrei,a Geoffrey G. Hicks,b James N. Reynolds,c,d Bhooma Thiruvahindrapuram,a 3 Worrawat Engchuan,a Molly Pind,b Sylvia Lamoureux,a John Wei,a Zhouzhi Wang,a Christian R. 4 Marshall,a Richard F. Wintle,a Albert E. Chudleye,f and Stephen W. Scherer,a,g 5 aThe Centre for Applied Genomics and Program in Genetics and Genome Biology, The Hospital 6 for Sick Children, Toronto, Ontario, Canada 7 bRegenerative Medicine Program, University of Manitoba, Winnipeg, Canada 8 cCentre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada.
    [Show full text]
  • A. Cellular Senescence
    Generation of antisense RNAs at convergent gene loci in cells undergoing senescence Maharshi Krishna Deb To cite this version: Maharshi Krishna Deb. Generation of antisense RNAs at convergent gene loci in cells undergo- ing senescence. Human genetics. Université Paul Sabatier - Toulouse III, 2016. English. NNT : 2016TOU30274. tel-03209213 HAL Id: tel-03209213 https://tel.archives-ouvertes.fr/tel-03209213 Submitted on 27 Apr 2021 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. 5)µ4& &OWVFEFMPCUFOUJPOEV %0$503"5%&-6/*7&34*5²%&506-064& %ÏMJWSÏQBS Université Toulouse 3 Paul Sabatier (UT3 Paul Sabatier) 1SÏTFOUÏFFUTPVUFOVFQBS DEB Maharshi Krishna -F mercredi 30 mars 2016 5Jtre : Generation of antisense RNAs at convergent gene loci in cells undergoing senescence École doctorale et discipline ou spécialité : ED BSB : Génétique moléculaire 6OJUÏEFSFDIFSDIF CNRS-UMR5088; LBCMCP %JSFDUFVS T EFʾÒTF Dr. TROUCHE Didier Co-Directeur/trice(s) de Thèse : Dr. NICOLAS Estelle 3BQQPSUFVST Prof. GILSON Eric, Dr. LIBRI Domenico, Dr. VERDEL Andre "VUSF T NFNCSF T EVKVSZ Prof. GLEIZES Pierre Emmanuel, President of Jury Dr. TROUCHE Didier, Thesis Supervisor This thesis is dedicated to any patients who may get cured with treatments manifesting from this work.
    [Show full text]
  • Single Cell Derived Clonal Analysis of Human Glioblastoma Links
    SUPPLEMENTARY INFORMATION: Single cell derived clonal analysis of human glioblastoma links functional and genomic heterogeneity ! Mona Meyer*, Jüri Reimand*, Xiaoyang Lan, Renee Head, Xueming Zhu, Michelle Kushida, Jane Bayani, Jessica C. Pressey, Anath Lionel, Ian D. Clarke, Michael Cusimano, Jeremy Squire, Stephen Scherer, Mark Bernstein, Melanie A. Woodin, Gary D. Bader**, and Peter B. Dirks**! ! * These authors contributed equally to this work.! ** Correspondence: [email protected] or [email protected]! ! Supplementary information - Meyer, Reimand et al. Supplementary methods" 4" Patient samples and fluorescence activated cell sorting (FACS)! 4! Differentiation! 4! Immunocytochemistry and EdU Imaging! 4! Proliferation! 5! Western blotting ! 5! Temozolomide treatment! 5! NCI drug library screen! 6! Orthotopic injections! 6! Immunohistochemistry on tumor sections! 6! Promoter methylation of MGMT! 6! Fluorescence in situ Hybridization (FISH)! 7! SNP6 microarray analysis and genome segmentation! 7! Calling copy number alterations! 8! Mapping altered genome segments to genes! 8! Recurrently altered genes with clonal variability! 9! Global analyses of copy number alterations! 9! Phylogenetic analysis of copy number alterations! 10! Microarray analysis! 10! Gene expression differences of TMZ resistant and sensitive clones of GBM-482! 10! Reverse transcription-PCR analyses! 11! Tumor subtype analysis of TMZ-sensitive and resistant clones! 11! Pathway analysis of gene expression in the TMZ-sensitive clone of GBM-482! 11! Supplementary figures and tables" 13" "2 Supplementary information - Meyer, Reimand et al. Table S1: Individual clones from all patient tumors are tumorigenic. ! 14! Fig. S1: clonal tumorigenicity.! 15! Fig. S2: clonal heterogeneity of EGFR and PTEN expression.! 20! Fig. S3: clonal heterogeneity of proliferation.! 21! Fig.
    [Show full text]
  • WO 2019/068007 Al Figure 2
    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/068007 Al 04 April 2019 (04.04.2019) W 1P O PCT (51) International Patent Classification: (72) Inventors; and C12N 15/10 (2006.01) C07K 16/28 (2006.01) (71) Applicants: GROSS, Gideon [EVIL]; IE-1-5 Address C12N 5/10 (2006.0 1) C12Q 1/6809 (20 18.0 1) M.P. Korazim, 1292200 Moshav Almagor (IL). GIBSON, C07K 14/705 (2006.01) A61P 35/00 (2006.01) Will [US/US]; c/o ImmPACT-Bio Ltd., 2 Ilian Ramon St., C07K 14/725 (2006.01) P.O. Box 4044, 7403635 Ness Ziona (TL). DAHARY, Dvir [EilL]; c/o ImmPACT-Bio Ltd., 2 Ilian Ramon St., P.O. (21) International Application Number: Box 4044, 7403635 Ness Ziona (IL). BEIMAN, Merav PCT/US2018/053583 [EilL]; c/o ImmPACT-Bio Ltd., 2 Ilian Ramon St., P.O. (22) International Filing Date: Box 4044, 7403635 Ness Ziona (E.). 28 September 2018 (28.09.2018) (74) Agent: MACDOUGALL, Christina, A. et al; Morgan, (25) Filing Language: English Lewis & Bockius LLP, One Market, Spear Tower, SanFran- cisco, CA 94105 (US). (26) Publication Language: English (81) Designated States (unless otherwise indicated, for every (30) Priority Data: kind of national protection available): AE, AG, AL, AM, 62/564,454 28 September 2017 (28.09.2017) US AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, 62/649,429 28 March 2018 (28.03.2018) US CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, (71) Applicant: IMMP ACT-BIO LTD.
    [Show full text]