Resultados totales (Incluyendo duplicados): 45302
Encontrada(s) 4531 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282712
Dataset. 2019

DIETARY STRESS INCREASES THE TOTAL OPPORTUNITY FOR SEXUAL SELECTION AND MODIFIES SELECTION ON CONDITION-DEPENDENT TRAITS

  • Cattelan, Silvia
  • Evans, Jonathan P.
  • García-González, Francisco
  • Morbiato, Elisa
  • Pilastro, Andrea
Although it is often expected that adverse environmental conditions depress the expression of condition-dependent sexually-selected traits, the full consequences of environmental change for the action of sexual selection, in terms of the opportunity for total sexual selection and patterns of phenotypic selection, are unknown. Here we show that dietary stress in guppies, Poecilia reticulata, reduces the expression of several sexually-selected traits and increases the opportunity for total sexual selection (standardized variance in reproductive success) in males. Furthermore, our results show that dietary stress modulates the relative importance of precopulatory (mating success) and postcopulatory (relative fertilization success) sexual selection, and that the form of multivariate sexual selection (linear vs. nonlinear) depends on dietary regime. Overall, our results are consistent with a pattern of heightened directional selection on condition-dependent sexually-selected traits under environmental stress, and underscore the importance of sexual selection in shaping adaptation in a changing world., Peer reviewed

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282736
Dataset. 2020

DENITRIFICATION RATES IN MOUNTAIN LAKE SEDIMENTS

  • Palacín, Carlos
  • Catalán, Jordi
[Methods] Dr Carlos Palacin-Lizarbe made the datasets. For further details of the methods see the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). Further details on the denitrification rate measurement method are provided in the publication: Palacin-Lizarbe, C., Camarero, L. & Catalan, J. Estimating sediment denitrification rates using cores and N2O microsensors. J Vis Exp, e58553, doi:doi:10.3791/58553 (2018). Further details on the sediment molecular descriptors (DNA, 16S, nirS, nirK, nosZ1, and nosZ2) are provided in the publication: Palacin-Lizarbe, C. et al. The DNRA-denitrification dichotomy differentiates nitrogen transformation pathways in mountain lake benthic habitats. Front Microbiol 10, 1229, doi:10.3389/fmicb.2019.01229 (2019)., [Usage Notes] Dryad repository - https://doi.org/10.5061/dryad.j6q573n95 This document describes four datasets (tab-separated text format tables) related to measurements of denitrification rates in mountain lake sediments of the Pyrenees. Dr Carlos Palacin-Lizarbe made the datasets, anyone that uses these data should reference this DRYAD repository and the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). See this publication for further details on the methods used. Dataset 1: Sediment_denitrification_rates_DRYAD.txt Dataset containing all measured denitrification rates and incubation conditions. The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, denitrification_rate, nitrate_treatment, temperature_insitu, temperature_incubation, nitrate_actual_plus_added, glucose_added, habitat, site. Dataset 2: table_background_DRYAD_def.txt Dataset containing all measured denitrification rates and the background descriptors (landscape, water, sediment). This dataset contain missing values. The dataset contains the following variables: id_core, yymmdd_lake_sample, actual_denitrification_rate, nitrate_add_7_denitrification_rate, nitrate_add_14_denitrification_rate, nitrate_add_28_denitrification_rate, high_nitrate_add_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrmicroogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. Dataset 3: table_actual_denitrification_rates_modeled_DRYAD.txt Dataset containing the actual denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, actual_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of actual denitrification rates, we use the variables scaled; before being scaled some variables were square root (actual_denitrification_rate, DOC, DNA, X16S, nirS, and nirK) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Dataset 4: table_potential_denitrification_rates_modeled_DRYAD.txt Dataset containing the potential (28 microM nitrate added) denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, nitrate_add_28_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of potential denitrification rates, we use the variables scaled; before being scaled some variables were square root (nitrate_add_28_denitrification_rate, DOC, DNA, X16S, and nirS) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nirK, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Variables contained in the datasets: id_rate: identification code for each denitrification rate measurement is useful as a link between datasets. id_core: identification code for each sediment core is useful as a link between datasets. yymmdd_lake_sample: The datasets are sorted by this variable. Identification code for each sediment core is useful as a link between datasets. Each code corresponds to the date sampled (year, month, and day), and the Lake, and the number of the sediment core. E.g. 130701Llo3 correspond the 3rd core sampled in Llong Lake on 2013, July 1 (13 07 01). The following are the sampled Lakes, with its abbreviation in brackets: Bergus (Be), Bassa de les Granotes (Bgra), Contraix (Co), Gelat de Bergus (GBe), Llebreta (Lle), Llong (Llo), Plan (Pl), Podo (Po), Redon (Re), Redo Aiguestortes (ReAT), and Redon de Vilamos (ReVil). This variable is useful as a link between datasets. denitrification_rate (micromols N2O m-2 h-1): denitrification rate without identifying the nitrate treatment. actual_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured without any substrate addition. nitrate_add_7_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 7 microM nitrate and 1.5 g/L of glucose. nitrate_add_14_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 14 microM nitrate and 1.5 g/L of glucose. nitrate_add_28_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 28 microM nitrate and 1.5 g/L of glucose. high_nitrate_add_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding a high concentration of nitrate (>300 microM) and 1.5 g/L of glucose. nitrate_treatment: treatment of nitrate addition in the denitrification measurements. Treatments: actual (without any nitrate added), Add_7 (7 microM nitrate added), Add_14 (14 microM nitrate added), Add_28 (28 microM nitrate added), High_add (>300 microM nitrate added). temperature_incubation (Celsius degrees): constant temperature during the incubation of the denitrification measurement. nitrate_actual_plus_added (microM): actual (in situ) plus added nitrate concentration in the water overlying the sediment core. glucose_added (mg * L-1): added when nitrate was also added. site: Lake (code): Bergus (B), Contraix (C), Bassa de les Granotes (G), Gelat de Bergus (GB), Llebreta (Le), Llong (Lo), Plan (P), Podo (Po), Redon (R), Redo Aiguestortes (RA), and Redon de Vilamos (RV). latitude (N). longitude (E). altitude (m a.s.l.). lake_area (ha). catchment_area (ha). renewal_time (months): mean time that water spends in the lake, retention time and residence time are synonyms of renewal time. habitat: sediment type (code): littoral sediments from rocky areas (R), helophyte Carex rostrata belts (C), beds of isoetid (I) and elodeid (E) macrophytes, and non-vegetated deep (D) sediments. temperature_insitu (Celsius degrees): temperature measured in the water overlying the sediment core just after the sediment core was retrieved. nitrate (microM): nitrate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. nitrite (microM): nitrite concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. ammonium (microM): ammonium concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. sulphate (microM): sulphate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. DOC (mg*L-1): dissolved organic carbon concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. water_column_depth (m): depth of the water column in the sediment core sampling point. Coded as 0.5 for littoral habitats. sediment_layer_depth (cm): depth of the sediment layer described by molecular and abiotic features. organic_matter (%): sediment organic matter content determined by Lost on Ignition. nitrogen (%): sediment nitrogen content. d15N (parts per thousand): sediment delta15N. carbon (%): sediment carbon content. d13C (parts per thousand): sediment delta13C. carbon_nitrogen_ratio (a/a): sediment atomic ratio of the two elements. dry_weight_wet_weight_ratio: sediment dry weight to wet weight ratio. sediment_density (g * cm-3). sediment_grain_size (um): sediment, volume diameter of the mean-sized spherical particle (vol_weighted_mean parameter provided by Mastersizer 2000, Malvern Instruments Ltd, UK). DNA (ng * m-2): sediment DNA content, ng of DNA per m2 in the sediment layer (0-0.5 cm). X16S (copies * m-2): 16S rRNA gene copies per m2 in the sediment layer (0-0.5 cm). nirS (copies * m-2) : nirS gene copies per m2 in the sediment layer (0-0.5 cm). nirK (copies * m-2) : nirK gene copies per m2 in the sediment layer (0-0.5 cm). nosZ1 (copies * m-2) : nosZ1 gene copies per m2 in the sediment layer (0-0.5 cm). nosZ2 (copies * m-2) : nosZ2 gene copies per m2 in the sediment layer (0-0.5 cm)., During the last decades, atmospheric nitrogen loading in mountain ranges of the Northern Hemisphere has increased substantially, resulting in high nitrate concentrations in many lakes. Yet, how increased nitrogen has affected denitrification, a key process for nitrogen removal, is poorly understood. We measured actual and potential (nitrate and carbon amended) denitrification rates in sediments of several lake types and habitats in the Pyrenees during the ice-free season. Actual denitrification rates ranged from 0 to 9 μmol N2O m−2 h−1 (mean, 1.5 ± 1.6 SD), whereas potential rates were about 10-times higher. The highest actual rates occurred in warmer sediments with more nitrate available in the overlying water. Consequently, littoral habitats showed, on average, 3-fold higher rates than the deep zone. The highest denitrification potentials were found in more productive lakes located at relatively low altitude and small catchments, with warmer sediments, high relative abundance of denitrification nitrite reductase genes, and sulphate-rich waters. We conclude that increased nitrogen deposition has resulted in elevated denitrification rates, but not sufficiently to compensate for the atmospheric nitrogen loading in most of the highly oligotrophic lakes. However, there is potential for high rates, especially in the more productive lakes and landscape features largely govern this., Ministerio de Ciencia e Innovación, Gobierno de España, Award: CGL2010-19373. Ministerio de Economía, Industria y Competitividad, Gobierno de España, Award: CGL2016–80124-C2-1-P., Peer reviewed


Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282741
Dataset. 2022

RAW DATA OF MANUSCRIPT “NITRO-OLEIC ACID REGULATES T CELL ACTIVATION THROUGH POST-TRANSLATIONAL MODIFICATION OF CALCINEURIN”

  • Bagó, Ángel
  • Cayuela, Laura
  • Gil, Alba
  • Calvo, Enrique
  • Vázquez, Jesús
  • Queiro, Antonio
  • Schopfer, Francisco J.
  • Radi, Rafael
  • Serrador, Juan M.
  • Íñiguez, Miguel Ángel
Methods used for generation of data are described under the Material and Methods section of the manuscript and the Supplementary Methods of the Supplementary Information Text., Dataset files of this paper will be shared by the corresponding authors upon reasonable request to the e-mail addresses jmserrador@cbm.csic.es and mainiguez@cbm.csic.es. The petitioner should compromise to neither copy, modify or distribute any file without the consent of the authors., El conjunto de datos primarios de esta publicación podrá ser compartido por los autores de correspondencia una vez solicitado a las direcciones de correo jmserrador@cbm.csic.es y mainiguez@cbm.csic.es y efectuado el compromiso de no distribución, copia o modificación de archivos sin la autorización de los autores., Nitro-fatty acids (NO2-FAs) are unsaturated fatty acid nitration products that exhibit anti-inflammatory actions in experimental mouse models of autoimmune and allergic diseases. These electrophilic molecules interfere with intracellular signaling pathways by reversible post-translational modification of nucleophilic amino-acid residues. Several regulatory proteins have been identified as targets of NO2-FAs, modifying their activity and promoting gene expression changes that result in anti-inflammatory effects. Herein, we report the effects of nitro-oleic acid (NO2-OA) on pro-inflammatory T cell functions, showing that 9- and 10-nitro-oleic acid, but not their oleic acid precursor, decrease T cell proliferation, expression of activation markers CD25 and CD71 on the plasma membrane and IL-2, IL-4 and IFN-γ cytokine gene expression. Moreover, we have found that NO2-OA inhibits the transcriptional activity of nuclear factor of activated T cells (NFAT) and that this inhibition takes place through the regulation of the phosphatase activity of calcineurin (CaN), hindering NFAT dephosphorylation, and nuclear translocation in activated T cells. Finally, using mass spectrometry-based approaches, we have found that NO2-OA nitroalkylates calcineurin A (CaNA) on four Cys (Cys129, 228, 266, and 372), of which only nitroalkylation on Cys372 was of importance for the regulation of CaN phosphatase activity in cells, disturbing functional CaNA/CaNB heterodimer formation. These results underscore new mechanisms by which NO2-FAs exert their anti-inflammatory actions, pointing to their potential as therapeutic bioactive lipids for the modulation of harmful T cell-mediated immune responses., RTI2018-100815-B-I00 (MICIU/FEDER) and the accompanying 2021 CSIC Exceptional Grant to M.A.I. and J.M.S. R01GM125944-05 and DK112854-04 to F.J.S., and Universidad de la República CSIC Grupos_2018, EI_2020 to R.R., MAIN FIGURES Figure 1: Figure 1A: Exp1: “Histogram_Exp1” and corresponding FACSfiles. Exp2: “Histogram_Exp2”, “Hig resolution”, “LowResolution_Exp2” and corresponding FACSfiles. Exp3: “Histogram Exp3” and corresponding FACSfiles. Exp4: “Histogram Exp4” and corresponding FACSfiles. “Fig1A_Graph”. “Fig1A_Histogram”. “Graph_Fig1A”. Figure 1B: Exp1: FACS files corresponding to CD69 and CD71. Exp2: FACS files corresponding to CD69 and CD71. Exp3: FACS files corresponding to CD69 and CD71. Fig1B_HistogramsCD71 and CD69: FACS files corresponding to Figure1B. “Fig1B_Graph”. “Graph_Fig1B”. Figure 1C: CTV CD4 CD8: FACS files corresponding to cell proliferation. CTV CD4 CD8II: FACS files corresponding to cell proliferation. Histogram_Fig 1C: FACS files and Histogram Figure 1C. Figure 1D: “Graph_Fig1D”. “Fig1D_Graph”. Figure 1E_F: Exp1: FACS files corresponding to Figure 1E and F. Exp2: FACS files corresponding to Figure 1E and F. Exp3: FACS files corresponding to Figure 1E and F. “Fig1E_Graph” “Fig1F_Graph” “Graph_Fig1E_F” “Layout Th2” “Layout Th1” Figure 2 Figure 2A: “Data RT-PCR IL2IFNGIL4vsGAPDH Fig 2A” “RT-PCR IFNg vs GAPDH 100%” “RT-PCR IL2 vs GAPDH 100%” “RT-PCR IL4 vs GAPDH 100%” Figure 2B: IFNgData: “Data Expts IFNgLUC” and “Raw Data Fig 2B IFNgLuc” IL2Data: “Data Expts IL2LUC” and “Raw Data Fig 2B IL2Luc” IL4Data: “Data Expts IL4LUC” and “Raw Data Fig 2B IL4Luc” Figure 3 Figure 3A: NFATLucPMA+Ion: “Graph_Fig3A_PMAI” and “GraphFig3A_PMAI”. NFATLucRaji+SEB: “Graph_Fig3A_SEB” and “GraphFig3A_SEB”. Figure 3B: “Fig3B_Graph_white” and “Graph_Fig3B” Figure 3C: “Fig3C_Graph” and “Graph_Fig3C” Figure 3D: Exp: b-actin: 2 files WB b-actin. RCAN: 4 files WB inducible and constitutive RCAN. Quantification: 3 quantifications (3 image files, 3 graphs files and 3 excel files). Excel “Densitometric analysis_bActin”. Fig 3D. Figure 4 Figure 4A: Photomicrographs_Fig4A: imaging files. Quantitative imaging: imaging files analysed. “Nuclear_NFAT percentage”. “Quantification” Excel file. Figure 4B: Exp: Cytosol: 2 files WB dynamin II and 2 files WB NFATC2. Nucleus: 2 files WB LaminB1 and 2 files WB NFATC2. “Figure 4B”. Figure 4C: Exp: DynaminII: 3 files WB Dynamin II. NFAT: 4 files WB NFATC2. “Fig4C” Figure 4D: “CaNA_DCAM domains”. Figure 4E: “Graph_Fig4E” and “Fig4E_Graph”. Figure 4F: “Graph_Fig4F” and “Fig4F_Graph”. Figure 4G: “Graph_Fig4G” and “Fig4G_Graph”. Figure 4H: Exp: 5 FACSfiles and “Fig4H”. Figure 5 Figure 5A: Exp: 2 files WB nitroalkylation and 1 file WB Coomassie CaNA. “Fig5A”. Figure 5B: Exp: 2 files WB nitroalkylation and 1 file WB Coomassie DCAM- AI. “Fig 5B”. Figure 5C: “NitroalkylAA_Sequence_CaNA”. Figure 5D: “Graph_Fig5D”. “Dots Fig 5D”. “Fig5D” Figure 5E: ”Graph_Fig5E” and “Fig5E_Graph”. Figure 5F: Exp: 4 files WB Biotin_Nitroalkylation and 1 file Coomassie GST-DCAM. “Fig 5F”. “Quantification”. Figure 6 Figure 6A: Exp: CoIPCnB: 2 files WB CaNB. InputCnA: 4 files WB input CaNA. Input CaNB: 1 file WB input CaNB. IPCnA: 1 file WB IP CaNA. “Fig 6A”. “Quantification co_IP Fig6A”. Figure 6B: Exp: 2 files WB CaNB. 1 file WB input CaNB. 1 file Coomassie GST-DCAM-AI. “Fig 6B”. “quantification pull_down_Fig6B”. Figure 6C: Exp: 2 files WB CaNB. 1 file Input CaNB. 1 file Coomassie GST-DCAM-AI. “Fig 6C”. “Quantification Fig6C”. Figure 6D: Exp: CoIP_CaNB: 3 files WB CaNB. Input_CaNB: 1 file WB input CaNB. Input_DCAM_GFP: 2 files WB GFP-DCAM-AI. IP_DCAM_GFP: 2 files WB GFP-DCAM-AI. “Fig6D”. “Quantification Co_IPFig6D”., Peer reviewed

DOI: http://hdl.handle.net/10261/282741, https://doi.org/10.20350/digitalCSIC/14780
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282741
HANDLE: http://hdl.handle.net/10261/282741, https://doi.org/10.20350/digitalCSIC/14780
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282741
PMID: http://hdl.handle.net/10261/282741, https://doi.org/10.20350/digitalCSIC/14780
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282741
Ver en: http://hdl.handle.net/10261/282741, https://doi.org/10.20350/digitalCSIC/14780
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282741

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282794
Dataset. 2018

FIGURE 5 FROM: AGUIRRE MP, ORTEGO J, CORDERO PJ (2018) INFLUENCE OF GRAZING ON POPULATIONS OF THE SPECIALIST GRASSHOPPER MIOSCIRTUS WAGNERI INHABITING HYPERSALINE HABITATS IN LA MANCHA REGION, CENTRAL SPAIN. JOURNAL OF ORTHOPTERA RESEARCH 27(1): 75-81. HTTPS://DOI.ORG/10.3897/JOR.27.21064

  • Aguirre, María P.
  • Ortego, Joaquín
  • Cordero, Pedro J.
Related identifiers: Part of 10.3897/jor.27.21064, Figure 5 Relationship between number of Mioscirtus wagneri per square meter (ABUNDANCE) and A. Cover (%) of Suaeda vera (SEEPWEED), and B. Livestock droppings per square meter (DROPPINGS). Open circles may correspond to one or more overlapping data points., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/282794, https://doi.org/10.3897/jor.27.21064.figure5
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282794
HANDLE: http://hdl.handle.net/10261/282794, https://doi.org/10.3897/jor.27.21064.figure5
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282794
PMID: http://hdl.handle.net/10261/282794, https://doi.org/10.3897/jor.27.21064.figure5
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282794
Ver en: http://hdl.handle.net/10261/282794, https://doi.org/10.3897/jor.27.21064.figure5
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282794

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282811
Dataset. 2018

FIGURE 1 FROM: AGUIRRE MP, ORTEGO J, CORDERO PJ (2018) INFLUENCE OF GRAZING ON POPULATIONS OF THE SPECIALIST GRASSHOPPER MIOSCIRTUS WAGNERI INHABITING HYPERSALINE HABITATS IN LA MANCHA REGION, CENTRAL SPAIN. JOURNAL OF ORTHOPTERA RESEARCH 27(1): 75-81. HTTPS://DOI.ORG/10.3897/JOR.27.21064

  • Aguirre, María P.
  • Ortego, Joaquín
  • Cordero, Pedro J.
Related identifiers: Part of 10.3897/jor.27.21064, Figure 1 Map of the study area (Villacañas, Toledo Province, Central Spain) showing the location of the hypersaline lagoons Tirez and Peña Hueca., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/282811, https://doi.org/10.3897/jor.27.21064.figure1
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282811
HANDLE: http://hdl.handle.net/10261/282811, https://doi.org/10.3897/jor.27.21064.figure1
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282811
PMID: http://hdl.handle.net/10261/282811, https://doi.org/10.3897/jor.27.21064.figure1
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282811
Ver en: http://hdl.handle.net/10261/282811, https://doi.org/10.3897/jor.27.21064.figure1
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oai:digital.csic.es:10261/282811

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282818
Dataset. 2018

FIGURE 2 FROM: AGUIRRE MP, ORTEGO J, CORDERO PJ (2018) INFLUENCE OF GRAZING ON POPULATIONS OF THE SPECIALIST GRASSHOPPER MIOSCIRTUS WAGNERI INHABITING HYPERSALINE HABITATS IN LA MANCHA REGION, CENTRAL SPAIN. JOURNAL OF ORTHOPTERA RESEARCH 27(1): 75-81. HTTPS://DOI.ORG/10.3897/JOR.27.21064

  • Aguirre, María P.
  • Ortego, Joaquín
  • Cordero, Pedro J.
Related identifiers: Part of 10.3897/jor.27.21064, Figure 2 Alkali seepweed prairie (Suaeda vera) without grazing, typical habitat of Mioscirtus wagneri in the study area (Peña Hueca lagoon, Villacañas, Toledo province, Spain). Photo by P.J. Cordero., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/282818, https://doi.org/10.3897/jor.27.21064.figure2
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282818
HANDLE: http://hdl.handle.net/10261/282818, https://doi.org/10.3897/jor.27.21064.figure2
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282818
PMID: http://hdl.handle.net/10261/282818, https://doi.org/10.3897/jor.27.21064.figure2
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282818
Ver en: http://hdl.handle.net/10261/282818, https://doi.org/10.3897/jor.27.21064.figure2
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282818

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282820
Dataset. 2015

DATA FROM: LONG TELOMERES ARE ASSOCIATED WITH CLONALITY IN WILD POPULATIONS OF THE FISSIPAROUS STARFISH COSCINASTERIAS TENUISPINA

  • García-Cisneros, Álex
  • Pérez-Portela, R.
  • Almroth, Bethanie C.
  • Degerman, Sofie
  • Palacín, Cruz
  • Sköld, Helen Nilsson
Telomere measurements and genotypes Genotypes and telomere measurements from all the individuals are provided in two excel sheets. In the first sheet, named "Population data", reports the arm length, ct. values and genotypes of all individuals. Finally, the second sheet, reports the ct. values from different tissues in regenerating and non-regenerating arms. Hdy_Garcia-Cisneros 2015.xls, TelTelomeres usually shorten during an organism’s lifespan and have thus been used as an aging and health marker. When telomeres become sufficiently short, senescence is induced. The most common method of restoring telomere length is via telomerase reverse transcriptase activity, highly expressed during embryogenesis. However, although asexual reproduction from adult tissues has an important role in the life cycles of certain species, its effect on the aging and fitness of wild populations, as well as its implications for the long-term survival of populations with limited genetic variation, is largely unknown. Here we compare relative telomere length of 58 individuals from four populations of the asexually reproducing starfish Coscinasterias tenuispina. Additionally, 12 individuals were used to compare telomere lengths in regenerating and non-regenerating arms, in two different tissues (tube feet and pyloric cecum). The level of clonality was assessed by genotyping the populations based on 12 specific microsatellite loci and relative telomere length was measured via quantitative PCR. The results revealed significantly longer telomeres in Mediterranean populations than Atlantic ones as demonstrated by the Kruskal–Wallis test (K=24.17, significant value: P-value<0.001), with the former also characterized by higher levels of clonality derived from asexual reproduction. Telomeres were furthermore significantly longer in regenerating arms than in non-regenerating arms within individuals (pyloric cecum tissue: Mann–Whitney test, V=299, P-value<10−6; and tube feet tissue Student's t=2.28, P-value=0.029). Our study suggests that one of the mechanisms responsible for the long-term somatic maintenance and persistence of clonal populations is telomere elongation., Peer reviewed

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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282825
Dataset. 2018

FIGURE 4 FROM: AGUIRRE MP, ORTEGO J, CORDERO PJ (2018) INFLUENCE OF GRAZING ON POPULATIONS OF THE SPECIALIST GRASSHOPPER MIOSCIRTUS WAGNERI INHABITING HYPERSALINE HABITATS IN LA MANCHA REGION, CENTRAL SPAIN. JOURNAL OF ORTHOPTERA RESEARCH 27(1): 75-81. HTTPS://DOI.ORG/10.3897/JOR.27.21064

  • Aguirre, María P.
  • Ortego, Joaquín
  • Cordero, Pedro J.
Related identifiers: Part of 10.3897/jor.27.21064, Figure 4 Relationship between probability of presence of Mioscirtus wagneri in the transects (PRESENCE) and A. Cover (%) of Suaeda vera (SEEPWEED) for extreme values of livestock droppings per square meter (DROPPINGS), and B. Livestock droppings per square meter (DROPPINGS) for extreme values of cover (%) of S. vera (SEEPWEED)., Peer reviewed

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DOI: http://hdl.handle.net/10261/282825, https://doi.org/10.3897/jor.27.21064.figure4
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282825
HANDLE: http://hdl.handle.net/10261/282825, https://doi.org/10.3897/jor.27.21064.figure4
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282825
PMID: http://hdl.handle.net/10261/282825, https://doi.org/10.3897/jor.27.21064.figure4
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282825
Ver en: http://hdl.handle.net/10261/282825, https://doi.org/10.3897/jor.27.21064.figure4
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282825

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282826
Dataset. 2015

DATA FROM: MSAP MARKERS AND GLOBAL CYTOSINE METHYLATION IN PLANTS: A LITERATURE SURVEY AND COMPARATIVE ANALYSIS FOR A WILD GROWING SPECIES

  • Alonso, Conchita
  • Pérez, Ricardo
  • Bazaga, Pilar
  • Medrano, Mónica
  • Herrera, Carlos M.
Genome-wide cytosine methylation estimates of 200 plants of Helleborus foetidus obtained by HPLC and Methylation Scoring of MSAP data Genome-wide cytosine methylation estimates in young leaves of 200 plants of Helleborus foetidus obtained using HPLC and the percentage of cytosine methylation in CCGG sites obtained by Methylation Scoring of MSAP data. Methylation scoring (MS50) was calculated as the percentage of MSAP loci scored as methylated (condition II + III). HPLC_MS_data_doi_10.5061_dryad.04d0d.txt, Methylation of DNA cytosines affects whether transposons are silenced and genes are expressed, and is a major epigenetic mechanism whereby plants respond to environmental change. Analyses of methylation-sensitive amplification polymorphism (MS-AFLP or MSAP) have been often used to assess methyl-cytosine changes in response to stress treatments and, more recently, in ecological studies of wild plant populations. MSAP technique does not require a sequenced reference genome and provides many anonymous loci randomly distributed over the genome for which the methylation status can be ascertained. Scoring of MSAP data, however, is not straightforward, and efforts are still required to standardize this step to make use of the potential to distinguish between methylation at different nucleotide contexts. Furthermore, it is not known how accurately MSAP infers genome-wide cytosine methylation levels in plants. Here, we analyse the relationship between MSAP results and the percentage of global cytosine methylation in genomic DNA obtained by HPLC analysis. A screening of literature revealed that methylation of cytosines at cleavage sites assayed by MSAP was greater than genome-wide estimates obtained by HPLC, and percentages of methylation at different nucleotide contexts varied within and across species. Concurrent HPLC and MSAP analyses of DNA from 200 individuals of the perennial herb Helleborus foetidus confirmed that methyl-cytosine was more frequent in CCGG contexts than in the genome as a whole. In this species, global methylation was unrelated to methylation at the inner CG site. We suggest that global HPLC and context-specific MSAP methylation estimates provide complementary information whose combination can improve our current understanding of methylation-based epigenetic processes in nonmodel plants., Peer reviewed

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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282830
Dataset. 2015

DATA FROM: PALEO-ISLANDS AS REFUGIA AND SOURCES OF GENETIC DIVERSITY WITHIN VOLCANIC ARCHIPELAGOS: THE CASE OF THE WIDESPREAD ENDEMIC CANARINA CANARIENSIS (CAMPANULACEAE)

  • Mairal, Mario
  • Sanmartín, Isabel
  • Aldasoro, Juan José
  • Culshaw, Victoria
  • Manolopoulou, Ioanna
  • Alarcón, María Luisa
Matrix AFLPs Canarina canariensis Original Matrix of Canarina canariensis AFLPs matriz_595alelos.xlsx cpDNA_Canarina_canariensis NEXUS files for the concatenated cpDNA dataset of Canarina canariensis, Geographical isolation by oceanic barriers and climatic stability has been postulated as some of the main factors driving diversification within volcanic archipelagos. However, few studies have focused on the effect that catastrophic volcanic events have had on patterns of within-island differentiation in geological time. This study employed data from the chloroplast (cpDNA haplotypes) and the nuclear (AFLPs) genomes to examine the patterns of genetic variation in Canarina canariensis, an iconic plant species associated with the endemic laurel forest of the Canary Islands. We found a strong geographical population structure, with a first divergence around 0.8 Ma that has Tenerife as its central axis and divides Canarian populations into eastern and western clades. Genetic diversity was greatest in the geologically stable ‘palaeo-islands’ of Anaga, Teno and Roque del Conde; these areas were also inferred as the ancestral location of migrant alleles towards other disturbed areas within Tenerife or the nearby islands using a Bayesian approach to phylogeographical clustering. Oceanic barriers, in contrast, appear to have played a lesser role in structuring genetic variation, with intra-island levels of genetic diversity larger than those between-islands. We argue that volcanic eruptions and landslides after the merging of the palaeo-islands 3.5 Ma played key roles in generating genetic boundaries within Tenerife, with the palaeo-islands acting as refugia against extinction, and as cradles and sources of genetic diversity to other areas within the archipelago., Peer reviewed

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