13 research outputs found
PDE3 Inhibition Reduces Epithelial Mast Cell Numbers in Allergic Airway Inflammation and Attenuates Degranulation of Basophils and Mast Cells
Epithelial mast cells are generally present in the airways of patients with allergic asthma that are inadequately controlled. Airway mast cells (MCs) are critically involved in allergic airway inflammation and contribute directly to the main symptoms of allergic patients. Phosphodiesterase 3 (PDE3) tailors signaling of cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), which are critical intracellular second messenger molecules in various signaling pathways. This paper investigates the pathophysiological role and disease-modifying effects of PDE3 in mouse bone marrow-derived MCs (bmMCs), human LAD2- and HMC1 mast cell lines, human blood basophils, and peripheral blood-derived primary human MCs (HuMCs). In a chronic house dust mite (HDM)-driven allergic airway inflammation mouse model, we observed that PDE3 deficiency or PDE3 inhibition (PDE3i) therapy reduced the numbers of epithelial MCs, when compared to control mice. Mouse bone marrow-derived MCs (bmMCs) and the human HMC1 and LAD2 cell lines predominantly expressed PDE3B and PDE4A. BmMCs from Pde3−/− mice showed reduced loss of the degranulation marker CD107b compared with wild-type BmMCs, when stimulated in an immunoglobulin E (IgE)-dependent manner. Following both IgE-mediated and substance P-mediated activation, PDE3i-pretreated basophils, LAD2 cells, and HuMCs, showed less degranulation than diluent controls, as measured by surface CD63 expression. MCs lacking PDE3 or treated with the PDE3i enoximone exhibited a lower calcium flux upon stimulation with ionomycine. In conclusion PDE3 plays a critical role in basophil and mast cell degranulation and therefore its inhibition may be a treatment option in allergic disease
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BioTIME: A database of biodiversity time series for the Anthropocene.
MotivationThe BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables includedThe database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grainBioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).Time period and grainBioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurementBioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format.csv and .SQL
Antibodies against endogenous retroviruses promote lung cancer immunotherapy
B cells are frequently found in the margins of solid tumours as organized follicles in ectopic lymphoid organs called tertiary lymphoid structures (TLS). Although TLS have been found to correlate with improved patient survival and response to immune checkpoint blockade (ICB), the underlying mechanisms of this association remain elusive. Here we investigate lung-resident B cell responses in patients from the TRACERx 421 (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy) and other lung cancer cohorts, and in a recently established immunogenic mouse model for lung adenocarcinoma. We find that both human and mouse lung adenocarcinomas elicit local germinal centre responses and tumour-binding antibodies, and further identify endogenous retrovirus (ERV) envelope glycoproteins as a dominant anti-tumour antibody target. ERV-targeting B cell responses are amplified by ICB in both humans and mice, and by targeted inhibition of KRAS(G12C) in the mouse model. ERV-reactive antibodies exert anti-tumour activity that extends survival in the mouse model, and ERV expression predicts the outcome of ICB in human lung adenocarcinoma. Finally, we find that effective immunotherapy in the mouse model requires CXCL13-dependent TLS formation. Conversely, therapeutic CXCL13 treatment potentiates anti-tumour immunity and synergizes with ICB. Our findings provide a possible mechanistic basis for the association of TLS with immunotherapy response
'Click'-inspired chemistry in macromolecular science: matching recent progress and user expectations
This year, it has been a decade that the concept of click chemistry was pioneered in polymer and material science by the exploration of the synthetic scope of copper-catalyzed azide/alkyne cycloaddition (CuAAC), the click benchmark. The impact on the endeavors of polymer chemists has been substantial because the power of this concept, featuring modularity, orthogonality, and versatility for the design and synthesis of polymeric materials, was recognized very soon in macromolecular research groups worldwide. After this first burst of research activity, challenging the boundaries of CuAAC in terms of attainable polymer constructs, ongoing method development, and implementation, in response to the need for metal-free alternatives, resulted in a valuable toolbox of click-inspired conjugation methods. Because of the large diversity of employable reactions, applied in various polymeric systems, the first-time or occasional click user will be confronted with a burden of choice. Therefore, the principal aim of this Perspective is to clearly denote the recent progress of click-inspired chemistry in macromolecular science by detailed conceptual analysis and to provide some selection procedures, allowing potential users to readily match their expectations of click chemistry to the state-of-the-art. Consequently, first-time or occasional users should be able to identify and select the most appropriate click-inspired reaction for their purposes and eventually contribute to the next generations of advanced polymeric materials
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BioTIME: A database of biodiversity time series for the Anthropocene
Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km(2) (158 cm(2)) to 100 km(2) (1,000,000,000,000 cm(2)). Time period and grainBio: TIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.European Research Council; EU [AdG-250189, PoC-727440, ERC-SyG-2013-610028]; Natural Environmental Research Council [NE/L002531/1]; National Science Foundation [DEB-1237733, DEB-1456729, 9714103, 0632263, 0856516, 1432277, DEB 9705814, BSR-8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910, DEB 0963447, DEB-1546686, DEB-129764]; National Science Foundation (LTER) [DEB-1235828, DEB-1440297, DBI-0620409, DEB-9910514, DEB-1237517, OCE-0417412, OCE-1026851, OCE-1236905, OCE-1637396, DEB 1440409, DEB-0832652, DEB-0936498, DEB-0620652, DEB-1234162, DEB-0823293, OCE-9982105, OCE-0620276, OCE-1232779]; Fundacao para a Ciencia e Tecnologia [POPH/FSE SFRH/BD/90469/2012, SFRH/BD/84030/2012, PTDC/BIA-BIC/111184/2009]; Ciencia sem Fronteiras/CAPES [1091/13-1]; Instituto Milenio de Oceanografia [IC120019]; ARC Centre of Excellence [CE0561432]; NSERC Canada; CONICYT/FONDECYT [1160026, ICM PO5-002, 11110351, 1151094, 1070808, 1130511]; RSF [14-50-00029]; Gordon and Betty Moore Foundation [GBMF4563]; Catalan Government; Marie Curie Individual Fellowship [QLK5-CT2002-51518, MERG-CT-2004-022065]; CNPq [306170/2015-9, 475434/2010-2, 403809/2012-6, 561897/2010, 306595-2014-1]; FAPESP (Sao Paulo Research Foundation) [2015/10714-6, 2015/06743-0, 2008/10049-9, 2013/50714-0, 1999/09635-0 e 2013/50718-5]; EU CLIMOOR [ENV4-CT97-0694]; VULCAN [EVK2-CT2000-00094]; DFG [120/10-2]; Polar Continental Shelf Program; CENPES - PETROBRAS; FAPERJ [E-26/110.114/ 2013]; German Academic Exchange Service; New Zealand Department of Conservation; Wellcome Trust [105621/Z/14/Z]; Smithsonian Atherton Seidell Fund; Botanic Gardens and Parks Authority; Research Council of Norway; Conselleria de Innovacio, Hisenda i Economia; Yukon Government Herschel Island-Qikiqtaruk Territorial Park; UK Natural Environment Research Council ShrubTundra Grant [NE/M016323/1]; IPY; Memorial University; ArcticNet; Netherlands Organization for Scientific Research in the Tropics NWO [W84-194]; Ciencias sem Fronteiras and Coordenacao de Pessoal de Nivel Superior (CAPES, Brazil) [1091/13-1]; U.S. Fish and Wildlife Service/State Wildlife federal grant [T-15]; Australian Research Council Centre of Excellence for Coral Reef Studies [CE140100020]; Australian Research Council Future Fellowship [FT110100609]; University of Lodz; NSF DEB [1353139]; Catalan Government fellowships (DURSI) [1998FI-00596, 2001BEAI200208]; MECD Post-doctoral fellowship [EX2002-0022]; FONDECYT [1141037]; FONDAP [15150003]; [SFRH/BD/80488/2011]; [PD/BD/52597/2014]; [REN2000-0278/CCI]; [REN2001-003/GLO]; [CGL2016-79835-P]; [AGAUR SGR-2014453]; [SGR-2017-1005]; [FCT - SFRH / BPD / 82259 / 2011]; [OCE 95-21184]; [OCE-0099226]; [OCE 03-5234]; [OCE-0623874]; [OCE-1031061]; [OCE-1336206]; [DEB-1354563]; [OPP-1440435]12 month embargo; published online: 24 July 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
BioTIME:a database of biodiversity time series for the Anthropocene
Abstract
Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.
Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km² (158 cm²) to 100 km² (1,000,000,000,000 cm²).
Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.
Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
Software format: .csv and .SQL