Resultados totales (Incluyendo duplicados): 34416
Encontrada(s) 3442 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282563
Dataset. 2015

DATA FROM: DNA BARCODING AND MINIBARCODING AS A POWERFUL TOOL FOR FEATHER MITE STUDIES

  • Doña, Jorge
  • Díaz-Real, Javier
  • Mironov, Sergey
  • Bazaga, Pilar
  • Serrano, David
  • Jovani, Roger
host_distribution, Feather mites (Astigmata: Analgoidea and Pterolichoidea) are among the most abundant and commonly occurring bird ectosymbionts. Basic questions on the ecology and evolution of feather mites remain unanswered because feather mite species identification is often only possible for adult males, and it is laborious even for specialized taxonomists, thus precluding large-scale identifications. Here, we tested DNA barcoding as a useful molecular tool to identify feather mites from passerine birds. Three hundred and sixty-one specimens of 72 species of feather mites from 68 species of European passerine birds from Russia and Spain were barcoded. The accuracy of barcoding and minibarcoding was tested. Moreover, threshold choice (a controversial issue in barcoding studies) was also explored in a new way, by calculating through simulations the effect of sampling effort (in species number and species composition) on threshold calculations. We found one 200-bp minibarcode region that showed the same accuracy as the full-length barcode (602 bp) and was surrounded by conserved regions potentially useful for group-specific degenerate primers. Species identification accuracy was perfect (100%) but decreased when singletons or species of the Proctophyllodes pinnatus group were included. In fact, barcoding confirmed previous taxonomic issues within the P. pinnatus group. Following an integrative taxonomy approach, we compared our barcode study with previous taxonomic knowledge on feather mites, discovering three new putative cryptic species and validating three previous morphologically different (but still undescribed) new species., Peer reviewed

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282568
Dataset. 2015

DATA FROM: ANCIENT VICARIANCE AND CLIMATE-DRIVEN EXTINCTION EXPLAIN CONTINENTAL-WIDE DISJUNCTIONS IN AFRICA: THE CASE OF THE RAND FLORA GENUS CANARINA (CAMPANULACEAE)

  • Mairal, Mario
  • Pokorny, Lisa
  • Alarcón, María Luisa
  • Aldasoro, Juan José
  • Sanmartín, Isabel
Fig2A_cpDNA_Platycodoneae NEXUS and tre files with the analysis settings used in MrBayes inferred from the concatenated chloroplast dataset (psbJ-petA, trnL-trnF, petB-petD) for the Platycodoneae Fig2B_ITS_platycodoneae NEXUS and tre files with the analysis settings used in MrBayes inferred from the nuclear ribosomal dataset (ITS) for Platycodoneae Fig2C_ITS_4cpdna NEXUS and tre files with the analysis settings used in MrBayes inferred from the combined nuclear and chloroplast dataset (ITS, psbJ-petA, trnL-trnF, petB-petD, trnS-trnG) for Platycodoneae Nested-dating approach analysis Script (.xml) and tre files for the "Nested analyses" of all three linked datasets: Platycodoneae, C. eminii and C. canariensis Nested Analysis.zip Figure S1 Nexus and .tre files for the single-gene analyses of the Platycodoneae dataset. Figure S2 Nexus and .tre files for the single-gene analyses of the Canarina dataset, Transoceanic distributions have attracted the interest of scientists for centuries. Less attention has been paid to the evolutionary origins of ‘continent-wide’ disjunctions, in which related taxa are distributed across isolated regions within the same continent. A prime example is the ‘Rand Flora’ pattern, which shows sister taxa disjunctly distributed in the continental margins of Africa. Here, we explore the evolutionary origins of this pattern using the genus Canarina, with three species: C. canariensis, associated with the Canarian laurisilva, and C. eminii and C. abyssinica, endemic to the Afromontane region in East Africa, as case study. We infer phylogenetic relationships, divergence times and the history of migration events within Canarina using Bayesian inference on a large sample of chloroplast and nuclear sequences. Ecological niche modelling was employed to infer the climatic niche of Canarina through time. Dating was performed with a novel nested approach to solve the problem of using deep time calibration points within a molecular dataset comprising both above-species and population-level sampling. Results show C. abyssinica as sister to a clade formed by disjunct C. eminii and C. canariensis. Miocene divergences were inferred among species, whereas infraspecific divergences fell within the Pleistocene–Holocene periods. Although C. eminii and C. canariensis showed a strong genetic geographic structure, among-population divergences were older in the former than in the latter. Our results suggest that Canarina originated in East Africa and later migrated across North Africa, with vicariance and aridification-driven extinction explaining the 7000 km/7 million year divergence between the Canarian and East African endemics., Peer reviewed

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282574
Dataset. 2015

DATA FROM: TRANSGENERATIONAL EFFECTS OF SEXUAL INTERACTIONS AND SEXUAL CONFLICT: NON-SIRES BOOST THE FECUNDITY OF FEMALES IN THE FOLLOWING GENERATION

  • García-González, Francisco
  • Dowling, Damian K.
All data for Transgenerational Effects manuscript All data for the manuscript in an Excel file. Details can be found in the first spreadsheet and ESM. DRYAD Transgenerational_effects_Drosophila.xlsx, The consequences of sexual interactions extend beyond the simple production of offspring. These interactions typically entail direct effects on female fitness, but may also impact the life histories of later generations. Evaluating the cross-generational effects of sexual interactions provides insights into the dynamics of sexual selection and conflict. Such studies can elucidate whether offspring fitness optima diverge across sexes upon heightened levels of sexual interaction among parents. Here, we found that, in Drosophila melanogaster, components of reproductive success in females, but not males, were contingent on the nature of sexual interactions experienced by their mothers. In particular, maternal sexual interactions with non-sires enhanced female fecundity in the following generation. This highlights the importance of non-sire influences of sexual interactions on the expression of offspring life histories., Peer reviewed

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282816
Dataset. 2015

DATA FROM: BEES EXPLAIN FLORAL VARIATION IN A RECENT RADIATION OF LINARIA

  • Blanco-Pastor, José Luis
  • Ornosa, Concepción
  • Romero, Daniel
  • Liberal, Isabel
  • Gómez, José M.
  • Vargas, Pablo
Raw data for accumulation curves_Dryad Datasets included in Supplementary Tables_Dryad Supplementary Tables_Dryad.docx, The role of pollinators in floral divergence has long attracted the attention of evolutionary biologists. Although abundant studies have reported the effect of pollinators on flower shape variation and plant speciation, the influence of pollinators on plant species differentiation during rapid radiations and the specific consequences of shifts among similar pollinators are not well understood. Here, we evaluate the association between pollinators and floral morphology in a closely related and recently diversifying clade of Linaria species (sect. Supinae subsect. Supinae). Our approach combined pollinator observations, functional floral morphometric measures and phylogenetic comparative analyses. The fauna visiting Linaria species was determined by extensive surveys and categorized by a modularity algorithm, while the size and shape of flowers were analyzed by means of standard and geometric morphometric measures. Standard measures failed to find relationships between the sizes of representative pollinators and flowers. However, discriminant-function analyses of geometric morphometric data revealed that pollination niches are finer predictors of flower morphologies in Linaria if compared with phylogenetic relationships. Species with the most restrictive flowers displayed the most slender spurs and were pollinated by bees with larger proboscides. These restrictive flower shapes likely appeared more than once during the evolutionary history of the study group. We show that floral variation can be driven by shifts between pollinators that have been traditionally included in a single functional group, and discuss the consequences of such transitions for plant species differentiation during rapid radiations., Peer reviewed

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282685
Dataset. 2022

BIOGEOGRAPHY OF BIRD AND MAMMAL TROPHIC STRUCTURES

  • Mendoza, Manuel
  • Araujo, Miguel B.
[Methods] How was this dataset collected? How has it been processed? Three sources of geographical data were extracted and plotted in a world terrestrial 1º×1º grid system: (1) global distributional ranges of non-marine mammal and bird species; (2) bioclimatic variables; and (3) net primary productivity. The global species distributions were derived from IUCN Global Assessment distributional data for native ranges (IUCN 2014). Specific occurrences in grid cells were used to produce a presence/absence matrix with names of 9993 non-marine birds and 5272 terrestrial mammals (15265 species) as columns and 15370 1º × 1º grid cells as rows. In the global species-level compilation published by Wilman et al. (2014), trophic resources are classified into 10 categories. The trophic profile of each species (Data_I) is obtained from the estimated percentage of each type of resource in their diet. The result is a matrix with the 10 trophic resources categories as columns, 15265 species of birds and mammals as rows, and values representing the estimated percentage of each type of resource. The eleventh column in Data_I is the trophic guild (TG) in which these species were classified. These trophic guilds were obtained using c-means clustering, on the basis of the Euclidean distance between the 15265 species in the 10-dimensional ‘species-level trophic space’ defined by the estimated percentage of each type of resource in their diet. In Data_II, 15370 1º × 1º terrestrial grid cells are identified by their coordinates (latitude and longitude). To obtain their trophic profile, we assigned species to their correspondent guild (Data_I) and then counted the number of species of each guild within the cell. The result is a matrix with the 9 trophic guilds as columns, 15370 communities as rows, and values representing numbers of species. The trophic profile of every community is thus a point in a 9-dimensional ‘trophic space’ defined by the number of species from each trophic guild (a vector of dimension 9). The tenth column in Data_II is the type of trophic structure found in the cell. These trophic structures (TS1 to TS6) correspond to well-defined clusters across this 9-dimensional ‘community-level trophic space’ identified with the help of the AMD index. Bioclimatic data for the terrestrial surface of the Earth (Data_III) were obtained from WorldClim - Global Climate Data (Hijmans et al. 2005)., Does climate determine the trophic organization of communities around the world? A recent study showed that a limited number of community trophic structures emerge when co-occurrence of trophic guilds among large mammals is examined globally. We ask whether the pattern is general across a all terrestrial mammals (n=5272) and birds (n=9993). We found that the six community-trophic structures previously identified with large mammals are largely maintained when all mammals and birds are examined, both together and separately, and that bioclimatic variables, including net primary productivity (NPP), are strongly related to variation in the geographical boundaries of community trophic structures. We argue that results are consistent with the view that trophic communities are self-organized structures optimizing energy flows, and that climate likely acts as the main control parameter by modulating the amount of solar energy available for conversion by plants and percolated through food webs across trophic communities. Gradual changes in climate parameters would thus be expected to trigger abrupt changes in energy flows resulting from phase transitions (tipping points) between different dynamical stable states. We expect future research to examine if our results are general across organisms, ecosystems, scales, and methodologies, and whether inferences rooted in complex systems theory are supported. The emergence of general patterns in the functional properties of animal communities at broad scales supports the emergence of food-web biogeography as a sub-discipline of biogeography focused on the analysis of the geographical distributions of trophic relationships among organisms., Spanish Ministry of Science, Innovation, and Universities., Peer reviewed

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282689
Dataset. 2022

BIOGEOGRAPHY OF BIRD AND MAMMAL TROPHIC STRUCTURES

SUPPORTING INFORMATION FOR THE PAPER

  • Mendoza, Manuel
  • Araujo, Miguel B.
Supplemental Information., Does climate determine the trophic organization of communities around the world? A recent study showed that a limited number of community trophic structures emerge when co-occurrence of trophic guilds among large mammals is examined globally. We ask whether the pattern is general across a all terrestrial mammals (n=5272) and birds (n=9993). We found that the six community-trophic structures previously identified with large mammals are largely maintained when all mammals and birds are examined, both together and separately, and that bioclimatic variables, including net primary productivity (NPP), are strongly related to variation in the geographical boundaries of community trophic structures. We argue that results are consistent with the view that trophic communities are self-organized structures optimizing energy flows, and that climate likely acts as the main control parameter by modulating the amount of solar energy available for conversion by plants and percolated through food webs across trophic communities. Gradual changes in climate parameters would thus be expected to trigger abrupt changes in energy flows resulting from phase transitions (tipping points) between different dynamical stable states. We expect future research to examine if our results are general across organisms, ecosystems, scales, and methodologies, and whether inferences rooted in complex systems theory are supported. The emergence of general patterns in the functional properties of animal communities at broad scales supports the emergence of food-web biogeography as a sub-discipline of biogeography focused on the analysis of the geographical distributions of trophic relationships among organisms., Funding provided by: Spanish Ministry of Science, Innovation, and Universities., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/282689
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282689
HANDLE: http://hdl.handle.net/10261/282689
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282689
PMID: http://hdl.handle.net/10261/282689
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282689
Ver en: http://hdl.handle.net/10261/282689
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282689

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282691
Dataset. 2020

DATA FROM: NATURAL HAZARDS AND WILDLIFE HEALTH: THE EFFECTS OF A VOLCANIC ERUPTION ON THE ANDEAN CONDOR

  • Plaza, Pablo
  • Wiemeyer, Guillermo
  • Blanco, Guillermo
  • Alarcón, Pablo
  • Hornero-Méndez, Dámaso
  • Donázar, José A.
  • Sánchez-Zapata, José A.
  • Hiraldo, Fernando
  • De La Rosa, Jesús D.
  • Lambertucci, Sergio A.
Volcanic eruptions produce health changes in animals that may be associated with emitted gases and deposited ashes. We evaluated whether the Puyehue–Cordón Caulle volcanic eruption in 2011 produced health changes in the threatened Andean condor (Vultur gryphus) living in the area most affected by the eruption, north-western Patagonia. We studied clinical and biochemical parameters of condors examined before and after the eruption. We also examined concentrations of different metals and metalloids in the blood of individuals sampled after the eruption. The most common clinical abnormality associated with the eruptive process was irritating pharyngitis. In condors sampled after the eruption, blood concentrations of albumin, calcium, carotenoids and total proteins decreased to levels under the reference values reported for this species. We found different chemical elements in the blood of these condors after the eruption, such as arsenic and cadmium, with the potential to produce health impacts. Thus, the health of Andean condors was affected in different ways by the eruption; remaining in the affected area appears to have been costly. However, in comparison to other animal species, the health impacts were not as strong and were mainly related to food shortages due to the decrease in availability of livestock carcasses linked to the eruption. This suggests that condors dealt relatively well with this massive event. Future research is needed to evaluate if the health changes we found reduce the survival of this species, and if the cost of inhabiting volcanic areas has any ecological or evolutionary influence on the condor’s life history., Peer reviewed

Proyecto: //

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

Buscador avanzado