Democratic Deficit, European Constitution, and a Vision of the Federal Europe: The EUs Path after the Lisbon Treaty

With the implementation of the Lisbon Treaty in December 2009, it has become more feasible to envisage a federal Europe through the establishment of an ever closer union as a political entity. Although the recent EU appears more like confederal or intergovernmental than federal, the Lisbon Treaty makes it possible to postulate that the future integration process of the EU would be its advance toward a federal state. On the verge of ramification toward either a federal Europe or a durable confederation, the EU faces a critical agenda of democratic deficit, i.e., a lack of vertical accountability between European political elites and voluntarily participating European citizens. The current status of the EU is obviously unique in its structure of multi level governance. Sometimes this structure is evaluated positively, but the study of former confederations also indicates that a confederate system is not durable, and rather unstable and impermanent. If the EU wants to move in a federal direction beyond confederation, it should answer the question of democratic deficit, that is, how to find European citizens who are loyal enough to sustain an independent political community. This paper discusses a possible route for the EU after the Lisbon Treaty, especially with respect to issues related to the democratic deficit and to the necessity of devising a European constitution

Functional cooperativity between the trigger factor chaperone and the ClpXP proteolytic complex

A functional association is uncovered between the ribosome-associated trigger factor (TF) chaperone and the ClpXP degradation complex. Bioinformatic analyses demonstrate conservation of the close proximity of tig, the gene coding for TF, and genes coding for ClpXP, suggesting a functional interaction. The effect of TF on ClpXP-dependent degradation varies based on the nature of substrate. While degradation of some substrates are slowed down or are unaffected by TF, surprisingly, TF increases the degradation rate of a third class of substrates. These include λ phage replication protein λO, master regulator of stationary phase RpoS, and SsrA-tagged proteins. Globally, TF acts to enhance the degradation of about 2% of newly synthesized proteins. TF is found to interact through multiple sites with ClpX in a highly dynamic fashion to promote protein degradation. This chaperone–protease cooperation constitutes a unique and likely ancestral aspect of cellular protein homeostasis in which TF acts as an adaptor for ClpXP

Comparative proteomics: assessment of biological variability and dataset comparability

BACKGROUND: Comparative proteomics in bacteria are often hampered by the differential nature of dataset quality and/or inherent biological deviations. Although common practice compensates by reproducing and normalizing datasets from a single sample, the degree of certainty is limited in comparison of multiple dataset. To surmount these limitations, we introduce a two-step assessment criterion using: (1) the relative number of total spectra (R (TS)) to determine if two LC-MS/MS datasets are comparable and (2) nine glycolytic enzymes as internal standards for a more accurate calculation of relative amount of proteins. Lactococcus lactis HR279 and JHK24 strains expressing high or low levels (respectively) of green fluorescent protein (GFP) were used for the model system. GFP abundance was determined by spectral counting and direct fluorescence measurements. Statistical analysis determined relative GFP quantity obtained from our approach matched values obtained from fluorescence measurements. RESULTS: L. lactis HR279 and JHK24 demonstrates two datasets with an R (TS) value less than 1.4 accurately reflects relative differences in GFP levels between high and low expression strains. Without prior consideration of R (TS) and the use of internal standards, the relative increase in GFP calculated by spectral counting method was 3.92 ± 1.14 fold, which is not correlated with the value determined by the direct fluorescence measurement (2.86 ± 0.42 fold) with the p = 0.024. In contrast, 2.88 ± 0.92 fold was obtained by our approach showing a statistically insignificant difference (p = 0.95). CONCLUSIONS: Our two-step assessment demonstrates a useful approach to: (1) validate the comparability of two mass spectrometric datasets and (2) accurately calculate the relative amount of proteins between proteomic datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0561-9) contains supplementary material, which is available to authorized users

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

Bet Inhibitors And Its Potentiation With Bcl2 Or Hdac Inhibitors As Targeted Therapies For Cutaneous T-Cell Lymphoma

Cutaneous T-Cell Lymphoma (CTCL) is a non-Hodgkin lymphoma of skin-homing, usually CD4+, malignant T cells that may involve lymph nodes and peripheral blood in advanced stages. Peripheral blood involvement portends a worse clinical outcome and while several systemic therapies are approved for CTCL, relapses are common. Mutational analysis of CTCL cells has revealed frequent amplification of the MYC oncogene, and bromodomain and extraterminal (BET) protein inhibitors have been shown to repress MYC expression in various malignancies. Towards a potential novel therapy, we thus sought to examine the effect of BET inhibition on CTCL cells in vitro by testing drug effects on CTCL cell viability, apoptosis induction, and gene expression. Each of the four tested BET inhibitors (JQ1, ABBV-075, I-BET762, CPI-0610) consistently induced dose-dependent decreases in viability of isolated patient-derived CTCL cells and established CTCL cell lines (MyLa, Sez4, HH, Hut78). This effect was synergistically potentiated by combination of BET inhibition with BCL2 inhibition (e.g. venetoclax) or histone deacetylase (HDAC) inhibition (e.g. vorinostat or romidepsin). There was also a marked increase in caspase 3/7 activation when JQ1 was combined with either vorinostat, or romidepsin, confirming that the observed synergies are due in major part to induction of apoptosis. Furthermore, MYC and BCL2 expression were each synergistically repressed when CTCL cells were treated with JQ1 plus HDAC inhibitors, suggesting cooperative activities at the level of epigenetic regulation. Taken together, these data indicate that targeting BET proteins in CTCL represents a promising novel therapeutic strategy that may be substantially potentiated by combination with BCL2 or HDAC inhibition

FISH Panel for Leukemic Cutaneous T-Cell Lymphoma: Extended Patient Cohort Correlation with Blood Involvement and Clinical Outcomes

The genomic basis of cutaneous T-cell lymphoma has been characterized by gene copy number alterations and genomic sequencing, but there are few clinical tests that are being widely used to inform the diagnosis and prognosis of leukemic cutaneous T-cell lymphoma that may arise as a progression from mycosis fungoides or de novo as Sézary syndrome. An 11-gene FISH panel of TP53, RB1, DNMT3A, FAS, ZEB1, ARID1A, ATM, and CDKN2A deletions and MYC, signal transducer and activator of transcription gene (STAT)3/5B, and CARD11 amplifications was previously found to encapsulate >95% of gene copy number variations in leukemic cutaneous T-cell lymphoma. Through a retrospective analysis of patients with leukemic cutaneous T-cell lymphoma seen at the Yale Cancer Center from 2014 to 2020, we gathered the relevant genes as they became available and correlated them to factors with prognostic relevance as a proof of concept to show the potential utility in further developing a limited gene panel for prognosis. In this study, we show that the abnormal FISH results show an association with clinically relevant factors (blood stage, CD4:8 ratio, and percentage blood involvement) and have a nonsignificant statistical trend (>90%) toward correlation with overall survival. In addition, the previous cost-effective panels were signal transducer and activator of transcription (STAT)3/5B, MYC, TP53, and ARID1A. We now suggest adding RB1 and ZEB1 on the basis of our findings

Fintech et seniors Sud-Coréens : Une étude des facteurs d'acceptation

International audienceCette recherche a pour but d'explorer les facteurs d'acceptation de la Fintech chez les seniors. Différents courants théoriques traitent les variables qui influencent l'acceptation des innovations technologiques. Le modèle choisi au niveau de cette étude est inspiré du modèle d'acceptation de la technologie (MAT ou, plus fréquemment, TAM pour Technology Acceptance Model). Notre modèle inclut les variables de l'utilité perçue, de la facilité d'utilisation, de l'accessibilité, du coût d'accès, de l'esprit d'innovation et de l'incertitude. La validation empirique du modèle a été réalisée à l'aide des méthodes d'équations structurelles sur un échantillon de 457 adultes coréens. Une analyse statistique a été réalisée par les logiciels SPSS 23.0 et AMOS 18. Nos résultats montrent que l'acceptation de la Fintech par les seniors est influencée par l'utilité perçue, la facilité d'utilisation, l'esprit d'innovation et l'incertitude. Toutefois, l'accessibilité et le coût d'accès n'ont pas une influence statistiquement significative sur l'intention d'utilisation de la Fintech. Cette étude a des implications pour les Fintech puisqu'elle peut leur permettre d'adapter leurs solutions à ce segment du marché voire de développer des produits adaptés à cette population (assurance vie en ligne, solutions de gestion de patrimoine et de succession…)

Fintech et seniors Sud-Coréens : une étude des facteurs d’acceptation

International audienceCette recherche a pour but d'explorer les facteurs d'acceptation de la Fintech chez les seniors. Différents courants théoriques traitent les variables qui influencent l'acceptation des innovations technologiques. Le modèle choisi au niveau de cette étude est inspiré du modèle d'acceptation de la technologie (MAT ou, plus fréquemment, TAM pour Technology Acceptance Model). Notre modèle inclut les variables de l'utilité perçue, de la facilité d'utilisation, de l'accessibilité, du coût d'accès, de l'esprit d'innovation et de l'incertitude. La validation empirique du modèle a été réalisée à l'aide des méthodes d'équations structurelles sur un échantillon de 457 adultes coréens. Une analyse statistique a été réalisée par les logiciels SPSS 23.0 et AMOS 18. Nos résultats montrent que l'acceptation de la Fintech par les seniors est influencée par l'utilité perçue, la facilité d'utilisation, l'esprit d'innovation et l'incertitude. Toutefois, l'accessibilité et le coût d'accès n'ont pas une influence statistiquement significative sur l'intention d'utilisation de la Fintech. Cette étude a des implications pour les Fintech puisqu'elle peut leur permettre d'adapter leurs solutions à ce segment du marché voire de développer des produits adaptés à cette population (assurance vie en ligne, solutions de gestion de patrimoine et de succession…)