Genetics and Eyecare

Most, if not all, diseases have an underlying genetic contribution, therefore all clinicians, as health care providers, must have a basic understanding of genetics and competency to care and educate patients on their diseases, especially diseases with significant genetics basis. This report presents ten ophthalmic conditions that are known to be caused by mutations in single genes or combined defects in multiple genes. The main purpose is to introduce interns and clinicians in eye care to some ophthalmic genetics conditions, the core competency in genetics for all health care professionals, the resource available online for further reference, and answers to questions that patients may have about their ocular disorders

Decreased expression of RNA interference machinery, Dicer and Drosha, is associated with poor outcome in ovarian cancer patients

The clinical and functional significance of RNA interference (RNAi) machinery, Dicer and Drosha, in ovarian cancer is not known and was examined. Dicer and Drosha expression was measured in ovarian cancer cell lines (n=8) and invasive epithelial ovarian cancer specimens (n=111) and correlated with clinical outcome. Validation was performed with previously published cohorts of ovarian, breast, and lung cancer patients. Anti-Galectin-3 siRNA and shRNA transfections were used for in vitro functional studies. Dicer and Drosha mRNA and protein levels were decreased in 37% to 63% of ovarian cancer cell lines and in 60% and 51% of human ovarian cancer specimens, respectively. Low Dicer was significantly associated with advanced tumor stage (p=0.007), and low Drosha with suboptimal surgical cytoreduction (p=0.02). Tumors with both high Dicer and Drosha were associated with increased median patient survival (>11 years vs. 2.66 years for other groups; p<0.001). In multivariate analysis, high Dicer (HR=0.48; p=0.02), high-grade histology (HR=2.46; p=0.03), and poor chemoresponse (HR=3.95; p<0.001) were identified as independent predictors of disease-specific survival. Findings of poor clinical outcome with low Dicer expression were validated in separate cohorts of cancer patients. Galectin-3 silencing with siRNA transfection was superior to shRNA in cell lines with low Dicer (78-95% vs. 4-8% compared to non-targeting sequences), and similar in cell lines with high Dicer. Our findings demonstrate the clinical and functional impact of RNAi machinery alterations in ovarian carcinoma and support the use of siRNA constructs that do not require endogenous Dicer and Drosha for therapeutic applications

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

NIST Interlaboratory Study on Glycosylation Analysis of Monoclonal Antibodies: Comparison of Results from Diverse Analytical Methods

Glycosylation is a topic of intense current interest in the development of biopharmaceuticals because it is related to drug safety and efficacy. This work describes results of an interlaboratory study on the glycosylation of the Primary Sample (PS) of NISTmAb, a monoclonal antibody reference material. Seventy-six laboratories from industry, university, research, government, and hospital sectors in Europe, North America, Asia, and Australia submit- Avenue, Silver Spring, Maryland 20993; 22Glycoscience Research Laboratory, Genos, Borongajska cesta 83h, 10 000 Zagreb, Croatia; 23Faculty of Pharmacy and Biochemistry, University of Zagreb, A. Kovacˇ ic´ a 1, 10 000 Zagreb, Croatia; 24Department of Chemistry, Georgia State University, 100 Piedmont Avenue, Atlanta, Georgia 30303; 25glyXera GmbH, Brenneckestrasse 20 * ZENIT / 39120 Magdeburg, Germany; 26Health Products and Foods Branch, Health Canada, AL 2201E, 251 Sir Frederick Banting Driveway, Ottawa, Ontario, K1A 0K9 Canada; 27Graduate School of Advanced Sciences of Matter, Hiroshima University, 1-3-1 Kagamiyama Higashi-Hiroshima 739–8530 Japan; 28ImmunoGen, 830 Winter Street, Waltham, Massachusetts 02451; 29Department of Medical Physiology, Jagiellonian University Medical College, ul. Michalowskiego 12, 31–126 Krakow, Poland; 30Department of Pathology, Johns Hopkins University, 400 N. Broadway Street Baltimore, Maryland 21287; 31Mass Spec Core Facility, KBI Biopharma, 1101 Hamlin Road Durham, North Carolina 27704; 32Division of Mass Spectrometry, Korea Basic Science Institute, 162 YeonGuDanji-Ro, Ochang-eup, Cheongwon-gu, Cheongju Chungbuk, 363–883 Korea (South); 33Advanced Therapy Products Research Division, Korea National Institute of Food and Drug Safety, 187 Osongsaengmyeong 2-ro Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, 363–700, Korea (South); 34Center for Proteomics and Metabolomics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands; 35Ludger Limited, Culham Science Centre, Abingdon, Oxfordshire, OX14 3EB, United Kingdom; 36Biomolecular Discovery and Design Research Centre and ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), Macquarie University, North Ryde, Australia; 37Proteomics, Central European Institute for Technology, Masaryk University, Kamenice 5, A26, 625 00 BRNO, Czech Republic; 38Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; 39Department of Biomolecular Sciences, Max Planck Institute of Colloids and Interfaces, 14424 Potsdam, Germany; 40AstraZeneca, Granta Park, Cambridgeshire, CB21 6GH United Kingdom; 41Merck, 2015 Galloping Hill Rd, Kenilworth, New Jersey 07033; 42Analytical R&D, MilliporeSigma, 2909 Laclede Ave. St. Louis, Missouri 63103; 43MS Bioworks, LLC, 3950 Varsity Drive Ann Arbor, Michigan 48108; 44MSD, Molenstraat 110, 5342 CC Oss, The Netherlands; 45Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5–1 Higashiyama, Myodaiji, Okazaki 444–8787 Japan; 46Graduate School of Pharmaceutical Sciences, Nagoya City University, 3–1 Tanabe-dori, Mizuhoku, Nagoya 467–8603 Japan; 47Medical & Biological Laboratories Co., Ltd, 2-22-8 Chikusa, Chikusa-ku, Nagoya 464–0858 Japan; 48National Institute for Biological Standards and Control, Blanche Lane, South Mimms, Potters Bar, Hertfordshire EN6 3QG United Kingdom; 49Division of Biological Chemistry & Biologicals, National Institute of Health Sciences, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo 158–8501 Japan; 50New England Biolabs, Inc., 240 County Road, Ipswich, Massachusetts 01938; 51New York University, 100 Washington Square East New York City, New York 10003; 52Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, United Kingdom; 53GlycoScience Group, The National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland; 54Department of Chemistry, North Carolina State University, 2620 Yarborough Drive Raleigh, North Carolina 27695; 55Pantheon, 201 College Road East Princeton, New Jersey 08540; 56Pfizer Inc., 1 Burtt Road Andover, Massachusetts 01810; 57Proteodynamics, ZI La Varenne 20–22 rue Henri et Gilberte Goudier 63200 RIOM, France; 58ProZyme, Inc., 3832 Bay Center Place Hayward, California 94545; 59Koichi Tanaka Mass Spectrometry Research Laboratory, Shimadzu Corporation, 1 Nishinokyo Kuwabara-cho Nakagyo-ku, Kyoto, 604 8511 Japan; 60Children’s GMP LLC, St. Jude Children’s Research Hospital, 262 Danny Thomas Place Memphis, Tennessee 38105; 61Sumitomo Bakelite Co., Ltd., 1–5 Muromati 1-Chome, Nishiku, Kobe, 651–2241 Japan; 62Synthon Biopharmaceuticals, Microweg 22 P.O. Box 7071, 6503 GN Nijmegen, The Netherlands; 63Takeda Pharmaceuticals International Co., 40 Landsdowne Street Cambridge, Massachusetts 02139; 64Department of Chemistry and Biochemistry, Texas Tech University, 2500 Broadway, Lubbock, Texas 79409; 65Thermo Fisher Scientific, 1214 Oakmead Parkway Sunnyvale, California 94085; 66United States Pharmacopeia India Pvt. Ltd. IKP Knowledge Park, Genome Valley, Shamirpet, Turkapally Village, Medchal District, Hyderabad 500 101 Telangana, India; 67Alberta Glycomics Centre, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 68Department of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 69Department of Chemistry, University of California, One Shields Ave, Davis, California 95616; 70Horva´ th Csaba Memorial Laboratory for Bioseparation Sciences, Research Center for Molecular Medicine, Doctoral School of Molecular Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Egyetem ter 1, Hungary; 71Translational Glycomics Research Group, Research Institute of Biomolecular and Chemical Engineering, University of Pannonia, Veszprem, Egyetem ut 10, Hungary; 72Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way Newark, Delaware 19711; 73Proteomics Core Facility, University of Gothenburg, Medicinaregatan 1G SE 41390 Gothenburg, Sweden; 74Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Institute of Biomedicine, Sahlgrenska Academy, Medicinaregatan 9A, Box 440, 405 30, Gothenburg, Sweden; 75Department of Clinical Chemistry and Transfusion Medicine, Sahlgrenska Academy at the University of Gothenburg, Bruna Straket 16, 41345 Gothenburg, Sweden; 76Department of Chemistry, University of Hamburg, Martin Luther King Pl. 6 20146 Hamburg, Germany; 77Department of Chemistry, University of Manitoba, 144 Dysart Road, Winnipeg, Manitoba, Canada R3T 2N2; 78Laboratory of Mass Spectrometry of Interactions and Systems, University of Strasbourg, UMR Unistra-CNRS 7140, France; 79Natural and Medical Sciences Institute, University of Tu¨ bingen, Markwiesenstrae 55, 72770 Reutlingen, Germany; 80Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands; 81Division of Bioanalytical Chemistry, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, de Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; 82Department of Chemistry, Waters Corporation, 34 Maple Street Milford, Massachusetts 01757; 83Zoetis, 333 Portage St. Kalamazoo, Michigan 49007 Author’s Choice—Final version open access under the terms of the Creative Commons CC-BY license. Received July 24, 2019, and in revised form, August 26, 2019 Published, MCP Papers in Press, October 7, 2019, DOI 10.1074/mcp.RA119.001677 ER: NISTmAb Glycosylation Interlaboratory Study 12 Molecular & Cellular Proteomics 19.1 Downloaded from https://www.mcponline.org by guest on January 20, 2020 ted a total of 103 reports on glycan distributions. The principal objective of this study was to report and compare results for the full range of analytical methods presently used in the glycosylation analysis of mAbs. Therefore, participation was unrestricted, with laboratories choosing their own measurement techniques. Protein glycosylation was determined in various ways, including at the level of intact mAb, protein fragments, glycopeptides, or released glycans, using a wide variety of methods for derivatization, separation, identification, and quantification. Consequently, the diversity of results was enormous, with the number of glycan compositions identified by each laboratory ranging from 4 to 48. In total, one hundred sixteen glycan compositions were reported, of which 57 compositions could be assigned consensus abundance values. These consensus medians provide communityderived values for NISTmAb PS. Agreement with the consensus medians did not depend on the specific method or laboratory type. The study provides a view of the current state-of-the-art for biologic glycosylation measurement and suggests a clear need for harmonization of glycosylation analysis methods. Molecular & Cellular Proteomics 19: 11–30, 2020. DOI: 10.1074/mcp.RA119.001677.L

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Identification and characterization of differentially expressed genes following chronic lithium treatment in rat brain

grantor: University of TorontoThe precise mechanisms underlying the therapeutic actions of lithium in bipolar disorder remain unclear, however, substantial evidence suggests alterations in gene expression may be critical to its long-term mood stabilization. The primary objectives of this study were to screen and identify genes, whose expression is altered in rat frontal cortex following chronic lithium treatment, by mRNA differential display. The pharmacospecificity of lithium's effects on gene expression was subsequently evaluated in comparison to those of anticonvulsant mood stabilizers, valproate and carbamazepine, and non-mood stabilizing psychotropic drugs, haloperidol and imipramine. Analyses by mRNA differential display and northern blotting identified two lithium regulated genes (LRG), LRG1 and LRG2. In addition, a third LRG (LRG3) was serendipitously identified by rapid amplification of cDNA ends. Molecular cloning and DNA sequence analyses of LRG1, LRG2 and LRG3 revealed their identities as aldolase A, diphosphoinositol polyphosphate phosphohydrolase 2 (DIPP2) and CD151, respectively. The mRNA levels of aldolase A (and C isozyme) were significantly reduced by chronic (5 weeks) lithium or carbamazepine treatment. The DIPP2 mRNA levels were increased only by lithium, whereas CD151 mRNA levels were reduced by chronic lithium, valproate or carbamazepine treatment. No significant effect on the mRNA levels of any of these genes was evident following either short-term (1-week) lithium treatment or chronic administration of haloperidol and imipramine, demonstrating time dependence and pharmacological specificity of the lithium's effects. Chronic lithium treatment also reduced the cytosolic, but not particulate, protein levels of aldolase in rat frontal cortex. Although the pharmacotherapeutic relevance of the effects of lithium on the expression of these three genes remains to be further investigated, the observations of a potential role of these gene products in the inositol phosphoinositide metabolism and signaling suggest that lithium may act, at the level of gene expression, on different components of the phosphoinositide signaling cascades, thereby producing its therapeutic efficacy. This suggestion is consistent with the current notion that abnormalities in intracellular signaling, including phosphoinositide signaling, may contribute, at least in part, to the pathophysiology of bipolar disorder.Ph.D

What images reveal: A comparative study of science images between Australian and Taiwanese junior high school textbooks

From a social semiotic perspective, image designs in science textbooks are inevitably influenced by the sociocultural context in which the books are produced. The learning environments of Australia and Taiwan vary greatly. Drawing on social semiotics and cognitive science, this study compares classificational images in Australian and Taiwanese junior high school science textbooks. Classificational images are important kinds of images, which can represent taxonomic relations among objects as reported by Kress and van Leeuwen (Reading images: the grammar of visual design, 2006). An analysis of the images from sample chapters in Australian and Taiwanese high school science textbooks showed that the majority of the Taiwanese images are covert taxonomies, which represent hierarchical relations implicitly. In contrast, Australian classificational images included diversified designs, but particularly types with a tree structure which depicted overt taxonomies, explicitly representing hierarchical super-ordinate and subordinate relations. Many of the Taiwanese images are reminiscent of the specimen images in eighteenth century science texts representing “what truly is”, while more Australian images emphasize structural objectivity. Moreover, Australian images support cognitive functions which facilitate reading comprehension. The relationships between image designs and learning environments are discussed and implications for textbook research and design are addressed

Paralogous murine Nudt10 and Nudt11 genes have differential expression patterns but encode identical proteins that are physiologically competent diphosphoinositol polyphosphate phosphohydrolases.

We previously described paralogous human genes [NUDT10 and NUDT11 [where NUDT is (nucleoside diphosphate attached moiety 'X')-type motif, also known as the 'nudix'-type motif]] encoding type 3 diphosphoinositol polyphosphate phosphohydrolases (DIPP3) [Hidaka, Caffrey, Hua, Zhang, Falck, Nickel, Carrel, Barnes and Shears (2002) J. Biol. Chem. 277, 32730-32738]. Normally, gene duplication is redundant, and lacks biological significance. Is this true for the DIPP3 genes? We address this question by characterizing highly-conserved murine Nudt10 and Nudt11 homologues of the human genes. Thus these genes must have been duplicated prior to the divergence of primates and sciurognath rodents, approx. 115 million years ago, greatly exceeding the 4 million year half-life for inactivation of redundant paralogues; our data therefore indicate that the DIPP3 duplication is unusual in being physiologically significant. One possible functional consequence is gene neofunctionalization, but we exclude that, since Nudt10 and Nudt11 encode identical proteins. Another possibility is gene subfunctionalization, which we studied by conducting the first quantitative expression analysis of these genes. We demonstrated high Nudt10 expression in liver, kidney and testis; Nudt11 expression is primarily restricted to the brain. This differential, but complementary, expression pattern indicates that subfunctionalization is the evolutionary consequence of DIPP3 gene duplication. Our kinetic data argue that diphosphoinositol polyphosphates are more physiologically relevant substrates for DIPP3 than are either diadenosine hexaphosphate or 5-phosphoribosyl 1-pyrophosphate. Thus the significance of the Nudt10/Nudt11 duplication is specific hydrolysis of diphosphoinositol polyphosphates in a tissue-dependent manner

Platelet-Derived Growth Factor in the Ovarian Follicle Attracts the Stromal Cells of the Fallopian Tube Fimbriae.

During human ovulation, the fallopian tube fimbriae must move to the ovulation site to catch the oocyte. As the tissue-of-origin of the majority of ovarian high-grade serous carcinoma (HGSC), the fallopian tube fimbriae carrying a precursor cancer lesion may also approach the ovulatory site for metastasis. We hypothesize that platelet-derived growth factor (PDGF) in mature follicle fluid (FF) attracts the migration of PDGFR-expressing fimbriae toward the ovulating follicle. We observed that more PDGFR-β was expressed in the distal part than in the proximal parts of the fallopian tube, particularly in stromal cells in the lamina propria. The stromal cells, but not the epithelial cells, from normal fimbriae and fallopian tube HGSC were highly chemotactic to mature FF. The chemotactic activities were positively correlated with PDGF-BB and estradiol levels in FF and were abolished by a blocking antibody of PDGFR-β and by tyrosine kinase inhibitor imatinib. When PDGF-BB/AB was depleted from the FF, more than 80% of chemotaxis activities were diminished. This study suggests an ovarian follicle-directed and PDGF-dependent attraction of fallopian tube fimbriae before ovulation. The same mechanism may also be crucial for the ovarian homing of HGSC, which largely originates in the fimbriae