Downregulation of Long Non-coding RNA FALEC Inhibits Gastric Cancer Cell Migration and Invasion Through Impairing ECM1 Expression by Exerting Its Enhancer-Like Function

Long non-coding RNAs (lncRNAs) have been shown to play important roles in many human diseases. However, their functions and mechanisms in tumorigenesis and development remain largely unknown. Here, we demonstrated that focally amplified lncRNA in epithelial cancer (FALEC) was upregulated and significantly correlated with lymph node metastasis, TNM stage in gastric cancer (GC). Further experiments revealed that FALEC knockdown significantly inhibited GC cells migration and invasion in vitro. Mechanistic investigations demonstrated that small interfering RNA-induced silencing of FALEC decreased expression of the nearby gene extracellular matrix protein 1 (ECM1) in cis. Additionally, ECM1 and FALEC expression were positively correlated, and high levels of ECM1 predicted shorter survival time in GC patients. Our results suggest that the downregulation of FALEC significantly inhibited the migration and invasion of GC cells through impairing ECM1 expression by exerting an enhancer-like function. Our work provides valuable information and a novel promising target for developing new therapeutic strategies in GC

Rasgrp1 mutation increases naïve T-cell CD44 expression and drives mTOR-dependent accumulation of Helios+ T cells and autoantibodies

Missense variants are a major source of human genetic variation. Here we analyze a new mouse missense variant, Rasgrp1Anaef, with an ENU-mutated EF hand in the Rasgrp1 Ras guanine nucleotide exchange factor. Rasgrp1Anaef mice exhibit anti-nuclear autoantibodies and gradually accumulate a CD44hi Helios+ PD-1+ CD4+ T cell population that is dependent on B cells. Despite reduced Rasgrp1-Ras-ERK activation in vitro, thymocyte selection in Rasgrp1Anaef is mostly normal in vivo, although CD44 is overexpressed on naïve thymocytes and T cells in a T-cell-autonomous manner. We identify CD44 expression as a sensitive reporter of tonic mTOR-S6 kinase signaling through a novel mouse strain, chino, with a reduction-of-function mutation in Mtor. Elevated tonic mTOR-S6 signaling occurs in Rasgrp1Anaef naïve CD4+ T cells. CD44 expression, CD4+ T cell subset ratios and serum autoantibodies all returned to normal in Rasgrp1AnaefMtorchino double-mutant mice, demonstrating that increased mTOR activity is essential for the Rasgrp1Anaef T cell dysregulation

Rasgrp1 mutation increases naïve T-cell CD44 expression and drives mTOR-dependent accumulation of Helios⁺ T cells and autoantibodies

Missense variants are a major source of human genetic variation. Here we analyze a new mouse missense variant, Rasgrp1ᴬⁿᵃᵉᶠ, with an ENU-mutated EF hand in the Rasgrp1 Ras guanine nucleotide exchange factor. Rasgrp1ᴬⁿᵃᵉᶠ mice exhibit anti-nuclear autoantibodies and gradually accumulate a CD44hi Helios⁺ PD-1⁺ CD4⁺ T cell population that is dependent on B cells. Despite reduced Rasgrp1-Ras-ERK activation in vitro, thymocyte selection in Rasgrp1ᴬⁿᵃᵉᶠ is mostly normal in vivo, although CD44 is overexpressed on naïve thymocytes and T cells in a T-cell-autonomous manner. We identify CD44 expression as a sensitive reporter of tonic mTOR-S6 kinase signaling through a novel mouse strain, chino, with a reduction-of-function mutation in Mtor. Elevated tonic mTOR-S6 signaling occurs in Rasgrp1ᴬⁿᵃᵉᶠ naïve CD4⁺ T cells. CD44 expression, CD4⁺ T cell subset ratios and serum autoantibodies all returned to normal in Rasgrp1ᴬⁿᵃᵉᶠMtorᶜʰⁱⁿᵒ double-mutant mice, demonstrating that increased mTOR activity is essential for the Rasgrp1ᴬⁿᵃᵉᶠ T cell dysregulation

Rasgrp1 mutation increases naïve T-cell CD44 expression and drives mTOR-dependent accumulation of Helios+ T cells and autoantibodies

Missense variants are a major source of human genetic variation. Here we analyze a new mouse missense variant, Rasgrp1(Anaef), with an ENU-mutated EF hand in the Rasgrp1 Ras guanine nucleotide exchange factor. Rasgrp1(Anaef) mice exhibit anti-nuclear autoantibodies and gradually accumulate a CD44(hi) Helios(+) PD-1(+) CD4(+) T cell population that is dependent on B cells. Despite reduced Rasgrp1-Ras-ERK activation in vitro, thymocyte selection in Rasgrp1(Anaef) is mostly normal in vivo, although CD44 is overexpressed on naïve thymocytes and T cells in a T-cell-autonomous manner. We identify CD44 expression as a sensitive reporter of tonic mTOR-S6 kinase signaling through a novel mouse strain, chino, with a reduction-of-function mutation in Mtor. Elevated tonic mTOR-S6 signaling occurs in Rasgrp1(Anaef) naïve CD4(+) T cells. CD44 expression, CD4(+) T cell subset ratios and serum autoantibodies all returned to normal in Rasgrp1(Anaef)Mtor(chino) double-mutant mice, demonstrating that increased mTOR activity is essential for the Rasgrp1(Anaef) T cell dysregulation. DOI: http://dx.doi.org/10.7554/eLife.01020.00

The landscape of recombination in African Americans

Recombination, together with mutation, is the ultimate source of genetic variation in populations. We leverage the recent mixture of people of African and European ancestry in the Americas to build a genetic map measuring the probability of crossing-over at each position in the genome, based on about 2.1 million crossovers in 30,000 unrelated African Americans. At intervals of more than three megabases it is nearly identical to a map built in Europeans. At finer scales it differs significantly, and we identify about 2,500 recombination hotspots that are active in people of West African ancestry but nearly inactive in Europeans. The probability of a crossover at these hotspots is almost fully controlled by the alleles an individual carries at PRDM9 (P<10−245). We identify a 17 base pair DNA sequence motif that is enriched in these hotspots, and is an excellent match to the predicted binding target of African-enriched alleles of PRDM9

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

The Leaf Microbiome of Tobacco Plants across Eight Chinese Provinces

Leaf microorganism communities play significant roles in the process of plant growth, but the microbiome profiling of crop leaves is still a relatively new research area. Here, we used 16S rDNA sequencing to profile the microbiomes of 78 primary dried tobacco leaf samples from 26 locations in eight Chinese provinces. Our analyses revealed that the national leaf microbial communities contain 4473 operational taxonomic units (OTU) representing 1234 species, but there is a small, national core microbiome with only 14 OTU representing nine species. The function of this core microbiome is related to processes including nitrogen fixation, detoxification of diverse pollutants, and heavy-metal reduction. The leaf microorganism communities are obviously affected by local environments but did not exhibit obvious relationships to single ecological factors (e.g., temperature, precipitation). Our findings enhance the understanding of microbial diversity of tobacco leaves, which could be utilized for a variety of bioprocess, agricultural, and environmental detoxification applications

Identification of Vitis vinifera MYB transcription factors and their response against grapevine berry inner necrosis virus

Abstract Background The myeloblastosis (MYB) superfamily is the largest transcription factor family in plants that play diverse roles during stress responses. However, the biotic stress-responsive MYB transcription factors of the grapevine have not been systematically studied. In China, grapevine berries are often infected with the grapevine berry inner necrosis virus (GINV), which eventually reduces the nutritional quality and commodity value. Results The present study identified and characterized 265 VvMYB or VvMYB-related genes of the “Crimson seedless” grapevine. Based on DNA-binding domain analysis, these VvMYB proteins were classified into four subfamilies, including MYB-related, 2R-MYB, 3R-MYB, and 4R-MYB. Phylogenetic analysis divided the MYB transcription factors into 26 subgroups. Overexpression of VvMYB58 suppressed GINV abundance in the grapevine. Further qPCR indicated that among 41 randomly selected VvMYB genes, 12 were induced during GINV infection, while 28 were downregulated. These findings suggest that VvMYB genes actively regulate defense response in the grapevine. Conclusion A deeper understanding of the MYB TFs engaged in GINV defense response will help devise better management strategies. The present study also provides a foundation for further research on the functions of the MYB transcription factors