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Research data keyboard_double_arrow_right Dataset 2022 EnglishPANGAEA EC | NEONANO, WT, EC | ZF-HEALTHEC| NEONANO ,WT ,EC| ZF-HEALTHAuthors: Leibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; +1 AuthorsLeibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; Michel, Maximilian;Obesity is a world wide problem and evidence suggests, that early lifetime undernourishment of caloric restirction predispose an organism for obesity and metabolic syndrome. We have raised two cohorts of zebrafish in an obesogenic environment (DIO) and compared several metabolic markers with fish raised under caloric restriction (CR) or fish shifted from CR to DIO at different periods in their life. We have looked morphologically at standard length and weight and found that fish on DIO grow faster in both axes. Fish shifted from CR to DIO show catch-up growth and not compensatory growth when shifted at one month, 3 months or 9 months of age. We have further characterized central agrp expression and hyperphagia, adipose tissue by histology as well as uCT imaging, hepatic histology, metabolic rate mitochondrial function as well as feeding induced glucose levels. We find that fish in an obesogenic environment develop markers of obesity which are not exacerbated by ealry lifetime food restriction.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 EnglishPANGAEA EC | NEONANO, EC | ZF-HEALTH, WTEC| NEONANO ,EC| ZF-HEALTH ,WTAuthors: Leibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; +1 AuthorsLeibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; Michel, Maximilian;For µCT imaging, adult zebrafish were fixed and decalcified in Bouin's solution at room temperature for 7 days, stored in PBS and imaged using a micro-computed tomography (µCT) device (SkyScan1272, Bruker BioSpin GmbH, Ettlingen, Germany). Zebrafish were placed individually in 1.5ml Eppendorf tubes using and an ultra-focus scan over the whole body was performed in a full-rotation in step-and-shoot mode. 322 projections (1008x672 pixels, 4x4 binning) were acquired per subscan with an x-ray tube voltage of 60 kV, power 0.166 mA, aluminum filter 0.25 mm,exposure time of 363 ms, 6 averages and a object-source distance of 86 mm. All CT images were reconstructed at an isotropic voxel size of 18 µm using a Feldkamp type algorithm (filtered back-projection). Fat-containing regions were appear hypo intense in µCT data and were segmented using Imalytics Preclinical (Gremse-IT GmbH, Aachen, Germany (Gremse et al., 2016; doi:10.7150/thno.13624). The volumetric fat percentage was calculated as the ratio of subcutaneous adipose tissue (SAT) or visceral adipose tissue (VAT) fat volume compared to the entire volume of the body cavity anterior of the anal fin and expressed per skeletal segment. Fish were raised as previously reported (Leibold and Hammerschmidt, 2015) for the following conditions:CG1: compensatory or catch up growth shifted at 1 month of ageCG3: compensatory or catch up growth shifted at 3 months of ageCG9: compensatory or catch up growth shifted at 9 months of ageCR: caloric restrictionDIO: diet induced obesityThe CT .nii files correlate to the groups as follows: Group 2: CG1; Group 3: DIO1; Group 6: CG3; Group 7 DIO3; Group 10: CG9; Group 11: DIO9; Group 1: CR1; Group 5: CR3; Group 9: CR9
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Embargo end date: 30 Nov 2021 EnglishDryad UKRI | Born in Bradford 2nd Wave, EC | LIFECYCLE, EC | ESCAPE +6 projectsUKRI| Born in Bradford 2nd Wave ,EC| LIFECYCLE ,EC| ESCAPE ,WT ,EC| CHICOS ,EC| HELIX ,EC| ENVIROGENOMARKERS ,EC| ATHLETE ,EC| ENRIECOAuthors: Ruiz-Arenas, Carlos; Bustamante, Mariona;Ruiz-Arenas, Carlos; Bustamante, Mariona;To test associations between DNA methylation levels and gene expression levels in cis (cis eQTMs), we paired each Gene to CpGs closer than 500 kb from its TSS, either upstream or downstream. For each Gene, the TSS was defined based on HTA-2.0 annotation, using the start position for transcripts in the + strand, and the end position for transcripts in the - strand. CpGs position was obtained from Illumina 450K array annotation. Only CpGs in autosomal chromosomes (from chromosome 1 to 22) were tested. In the main analysis, we fitted for each CpG-Gene pair a linear regression model between gene expression and methylation levels adjusted for age, sex, cohort, and blood cell type composition. A second model was run without adjusting for blood cellular composition and it is only reported on the online web catalog, but not discussed in this manuscript. Although some of the unique associations of the unadjusted model might be real, others might be confounded by the large methylation and expression changes among blood cell types. To ensure that CpGs paired to a higher number of Genes do not have higher chances of being part of an eQTM, multiple-testing was controlled at the CpG level, following a procedure previously applied in the Genotype-Tissue Expression (GTEx) project (Gamazon et al., 2018). Briefly, our statistic used to test the hypothesis that a pair CpG-Gene is significantly associated is based on considering the lowest p-value observed for a given CpG and all its paired Gene (e.g., those in the 1 Mb window centered at the TSS). As we do not know the distribution of this statistic under the null, we used a permutation test. We generated 100 permuted gene expression datasets and ran our previous linear regression models obtaining 100 permuted p-values for each CpG-Gene pair. Then, for each CpG, we selected among all CpG-Gene pairs the minimum p-value in each permutation and fitted a beta distribution that is the distribution we obtain when dealing with extreme values (e.g. minimum) (Dudbridge and Gusnanto, 2008). Next, for each CpG, we took the minimum p-value observed in the real data and used the beta distribution to compute the probability of observing a lower p-value. We defined this probability as the empirical p-value of the CpG. Then, we considered as significant those CpGs with empirical p-values to be significant at 5% false discovery rate using Benjamini-Hochberg method. Finally, we applied a last step to identify all significant CpG-Gene pairs for all eCpGs. To do so, we defined a genome-wide empirical p-value threshold as the empirical p-value of the eCpG closest to the 5% false discovery rate threshold. We used this empirical p-value to calculate a nominal p-value threshold for each eCpG, based on the beta distribution obtained from the minimum permuted p-values. This nominal p-value threshold was defined as the value for which the inverse cumulative distribution of the beta distribution was equal to the empirical p-value. Then, for each eCpG, we considered as significant all eCpG-Gene variants with a p-value smaller than nominal p-value. For the meQTLs catalogue, we selected 9.9 M cis and trans meQTLs with a p-value <1e-7 in the ARIES dataset consisting of data from children of 7 years old (Gaunt et al., 2016). Then, we tested whether this subset of 9.9 M SNPs were also meQTLs in HELIX by running meQTL analyses using MatrixEQTL R package (Shabalin, 2012), adjusting for cohort, sex, age, blood cellular composition and the first 20 principal components (PCs) calculated from genome-wide genetic data of the GWAS variability. We confirmed 2.8 M meQTLs in HELIX (p-value <1e-7). Trans meQTLs represented <10% of the 2.8 M meQTLs. Enrichment of eCpGs for meQTLs was computed using a Chi-square test, using non eCpGs as background. Finally, we tested whether meQTLs were also eQTLs for the eGenes linked to the eCpGs. To this end, we run eQTL analyses (gene expression being the outcome and 2.8 M SNPs the predictors) with MatrixEQTL adjusting for cohort, sex, age, blood cellular composition and the first 20 GWAS PCs in HELIX. We considered as significant eQTLs the SNP-Gene pairs with p-value <1e-7 and with the direction of the effect consistent with the direction of the meQTL and the eQTM. Background: The identification of expression quantitative trait methylation (eQTMs), defined as associations between DNA methylation levels and gene expression, might help the biological interpretation of epigenome-wide association studies (EWAS). We aimed to identify autosomal cis eQTMs in children’s blood, using data from 832 children of the Human Early Life Exposome (HELIX) project. Methods: Blood DNA methylation and gene expression were measured with the Illumina 450K and the Affymetrix HTA v2 arrays, respectively. The relationship between methylation levels and expression of nearby genes (1 Mb window centered at the transcription start site, TSS) was assessed by fitting 13.6 M linear regressions adjusting for sex, age, cohort, and blood cell composition. Results: We identified 39,749 blood autosomal cis eQTMs, representing 21,966 unique CpGs (eCpGs, 5.7% of total CpGs) and 8,886 unique transcript clusters (eGenes, 15.3% of total transcript clusters, equivalent to genes). In 87.9% of these cis eQTMs, the eCpG was located at <250 kb from eGene’s TSS; and 58.8% of all eQTMs showed an inverse relationship between the methylation and expression levels. Only around half of the autosomal cis-eQTMs eGenes could be captured through annotation of the eCpG to the closest gene. eCpGs had less measurement error and were enriched for active blood regulatory regions and for CpGs reported to be associated with environmental exposures or phenotypic traits. 40.4% of eQTMs had at least one genetic variant associated with methylation and expression levels. The overlap of autosomal cis eQTMs in children’s blood with those described in adults was small (13.8%), and age-shared cis eQTMs tended to be proximal to the TSS and enriched for genetic variants. See HELIX_Blood_eQTM_READMEfile_20210205.xlsx.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Embargo end date: 13 Jul 2021 EnglishDryad EC | ColourFish, UKRI | Anisotropic retinal circu..., WT | How to connect an eye to ... +2 projectsEC| ColourFish ,UKRI| Anisotropic retinal circuits for processing of colour and space in nature ,WT| How to connect an eye to a brain ,WT ,EC| NeuroVisEcoSchröder, Cornelius; Yoshimatsu, Takeshi; Oesterle, Jonathan; Berens, Philipp; Baden, Tom;Many sensory systems use ribbon-type synapses to transmit their signals to downstream circuits. The properties of this synaptic transfer fundamentally dictate which aspects in the original stimulus will be accentuated or suppressed, thereby partially defining the detection limits of the circuit. Accordingly, sensory neurons have evolved a wide variety of ribbon geometries and vesicle pool properties to best support their diverse functional requirements. However, the need for diverse synaptic functions does not only arise across neuron types, but also within. Here we show that UV-cones, a single type of photoreceptor of the larval zebrafish eye, exhibit striking differences in their synaptic ultrastructure and consequent calcium to glutamate transfer function depending on their location in the eye. We arrive at this conclusion by combining serial section electron microscopy and simultaneous "dual-colour" 2-photon imaging of calcium and glutamate signals from the same synapse in vivo. We further use the functional dataset to fit a cascade-like model of the ribbon synapse with different vesicle pool sizes, transfer rates and other synaptic properties. Exploiting recent developments in simulation-based inference, we obtain full posterior estimates for the parameters and compare these across different retinal regions. The model enables us to extrapolate to new stimuli and to systematically investigate different response behaviours of various ribbon configurations. We also provide an interactive, easy-to-use version of this model as an online tool. Overall, we show that already on the synaptic level of single neuron types there exist highly specialized mechanisms which are advantageous for the encoding of different visual features.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Embargo end date: 04 Aug 2020 United Kingdom EnglishDryad EC | SVDs-at-target, WT, EC | CoSTREAM +1 projectsEC| SVDs-at-target ,WT ,EC| CoSTREAM ,EC| CVGENES-AT-TARGETGeorgakis, Marios; Gill, Dipender; Webb, Alastair; Evangelou, Evangelos; Elliott, Paul; Sudlow, Cathie; Dehghan, Abbas; Malik, Rainer; Tzoulaki, Ioanna; Dichgans, Martin;Objective: We employed Mendelian Randomization to explore whether the effects of blood pressure (BP) and BP lowering through different antihypertensive drug classes on stroke risk vary by stroke etiology. Methods: We selected genetic variants associated with systolic and diastolic BP and BP-lowering variants in genes encoding antihypertensive drug targets from a GWAS on 757,601 individuals. Applying two-sample Mendelian randomization, we examined associations with any stroke (67,162 cases; 454,450 controls), ischemic stroke and its subtypes (large artery, cardioembolic, small vessel stroke), intracerebral hemorrhage (ICH, deep and lobar), and the related small vessel disease phenotype of WMH. Results: Genetic predisposition to higher systolic and diastolic BP was associated with higher risk of any stroke, ischemic stroke, and ICH. We found associations between genetically determined BP and all ischemic stroke subtypes with a higher risk of large artery and small vessel stroke compared to cardioembolic stroke, as well as associations with deep, but not lobar ICH. Genetic proxies for calcium channel blockers, but not beta blockers, were associated with lower risk of any stroke and ischemic stroke. Proxies for CCBs showed particularly strong associations with small vessel stroke and the related radiological phenotype of WMH. Conclusions: This study supports a causal role of hypertension in all major stroke subtypes except lobar ICH. We find differences in the effects of BP and BP lowering through antihypertensive drug classes between stroke subtypes and identify calcium channel blockade as a promising strategy for preventing manifestations of cerebral small vessel disease. Objective: We employed Mendelian Randomization to explore whether the effects of blood pressure (BP) and BP lowering through different antihypertensive drug classes on stroke risk vary by stroke etiology. Methods: We selected genetic variants associated with systolic and diastolic BP and BP-lowering variants in genes encoding antihypertensive drug targets from a GWAS on 757,601 individuals. Applying two-sample Mendelian randomization, we examined associations with any stroke (67,162 cases; 454,450 controls), ischemic stroke and its subtypes (large artery, cardioembolic, small vessel stroke), intracerebral hemorrhage (ICH, deep and lobar), and the related small vessel disease phenotype of WMH. Results: Genetic predisposition to higher systolic and diastolic BP was associated with higher risk of any stroke, ischemic stroke, and ICH. We found associations between genetically determined BP and all ischemic stroke subtypes with a higher risk of large artery and small vessel stroke compared to cardioembolic stroke, as well as associations with deep, but not lobar ICH. Genetic proxies for calcium channel blockers, but not beta blockers, were associated with lower risk of any stroke and ischemic stroke. Proxies for CCBs showed particularly strong associations with small vessel stroke and the related radiological phenotype of WMH. Conclusions: This study supports a causal role of hypertension in all major stroke subtypes except lobar ICH. We find differences in the effects of BP and BP lowering through antihypertensive drug classes between stroke subtypes and identify calcium channel blockade as a promising strategy for preventing manifestations of cerebral small vessel disease. 1
Spiral - Imperial Co... arrow_drop_down Spiral - Imperial College Digital RepositoryDataset . 2020Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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visibility 73visibility views 73 download downloads 60 Powered bymore_vert Spiral - Imperial Co... arrow_drop_down Spiral - Imperial College Digital RepositoryDataset . 2020Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Mendeley WT | Wellcome Centre for Integ..., EC | LEARNING&ACHIEVEMENTWT| Wellcome Centre for Integrative Neuroimaging ,EC| LEARNING&ACHIEVEMENTAuthors: Zacharopoulos, George;Zacharopoulos, George;This dataset contains the structural and the behavioural metrics THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE
Mendeley Data; NARCI... arrow_drop_down Mendeley Data; NARCIS; DANS-EASYDataset . 2020add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert Mendeley Data; NARCI... arrow_drop_down Mendeley Data; NARCIS; DANS-EASYDataset . 2020add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Embargo end date: 05 May 2020 EnglishDryad UKRI | EPSRC and BBSRC Centre fo..., WT | Micron Oxford: super-reso..., UKRI | Life Sciences Interface D... +1 projectsUKRI| EPSRC and BBSRC Centre for Doctoral Training in Synthetic Biology ,WT| Micron Oxford: super-resolution imaging of cellular dynamics. ,UKRI| Life Sciences Interface Doctoral Training Centre ,EC| SYNTISUAuthors: Alcinesio, Alessandro; Krishna Kumar, Ravinash; Monico, Carina; Cazimoglu, Idil; +5 AuthorsAlcinesio, Alessandro; Krishna Kumar, Ravinash; Monico, Carina; Cazimoglu, Idil; Bayley, Hagan; Meacock, Oliver J.; Allan, Rebecca G.; Cornall, Matthew T.; Restrepo Schild, Vanessa;3D-printing networks of droplets connected by interface bilayers is a powerful platform to build synthetic tissues, in which functionality relies on precisely ordered structures. However, the structural precision and consistency in assembling these structures is currently limited, which restricts intricate designs and the complexity of functions performed by synthetic tissues. Here, we report that the equilibrium contact angle (θDIB) between a pair of droplets is a key parameter that dictates the tessellation and precise positioning of hundreds of picolitre droplets within 3D-printed, multi-layer networks. When θDIB approximates the geometrically-derived critical angle (θc) of 35.3º, the resulting networks of droplets arrange in regular hexagonally close-packed (hcp) lattices with the least fraction of defects. With this improved control over droplet packing, we can 3D-print functional synthetic tissues with single-droplet-wide conductive pathways. Our new insights into 3D droplet packing permit the fabrication of complex synthetic tissues, where precisely positioned compartments perform coordinated tasks. The files are labelled in the format: SILXX_POPCYY_NetNumber_Process.tif Where: "SILXX_POPCYY" indicates the volume fraction of silicone oil (φSIL) and molar fraction of POPC (xPOPC) at which the network was printed (e.g. "SIL55_POPC13" corresponds to φSIL = 0.55 and xPOPC = 0.13) "NetNumber" indicates the repeat number of the specific printed network (e.g. "net3" indicates the 3rd network printed at a specific condition) "Process" indicates the type of processing visualised in the image. This can be: " " : Raw confocal image "Network": Segmented image showing automatic identification of the lipid bilayers in droplet networks "Network_Corrected": Manually corrected image after automatic segmentation "LinkClasses": Segmented image showing classification of lipid bilayers and lipid monolayers "Overlay": Overlay of the confocal image and corresponding packing classification based on Delaunay triangulation.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Embargo end date: 08 Jan 2020 EnglishDryad WT, WT | Multi-scale and multi-mod..., NIH | MRI Toolbox for Rodent Br... +5 projectsWT ,WT| Multi-scale and multi-modal assessment of coupling in the healthy and diseased brain. ,NIH| MRI Toolbox for Rodent Brain Microstructure Imaging ,NIH| Center for Advanced Imaging Innovation and Research (CAI2R) ,UKRI| National Facility for In Vivo MR Imaging of Human Tissue Microstructure ,EC| DIRECT-fMRI ,NIH| Mesoscopic Biomarkers of Neurodegeneration with Diffusion MRI ,WT| Tractometry.Authors: Veraart, Jelle;Veraart, Jelle;Axon size plays a crucial role in determining conductance velocity and, consequently, in the the timing and synchronization of neural activation. Noninvasive measurement of axon radii could have significant impact on the understanding of healthy and diseased neural processes. However, until now, accurate axon radius mapping has eluded in vivo neuroimaging, mainly due to a lack of sensitivity of the MRI signal to micron-sized axons. Here, we show how -- when confounding factors such as extra-axonal water and axonal orientation dispersion are eliminated -- heavily diffusion-weighted MRI signals becomes sensitive to axon radii. However, diffusion MRI is only capable of estimating a single metric representing the entire axon radius distribution within a voxel that emphasizes the largest axons. Our findings, both in rodents and humans, enable noninvasive mapping of critical information on axon radii, as well as resolve the long-standing debate on whether axon radii can be quantified. The dataset is comprised of three main items: 1. Human diffusion MRI 2. Rodent diffusion MRI 3. Rodent confocal microscopy Please consider the information and technical details provided in the file "Documentation.pdf" when using the data. The data acquisition is decribed in the manuscript. Please contact the authors for additional information.
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visibility 47visibility views 47 download downloads 50 Powered bymore_vert ZENODO arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Embargo end date: 24 Jul 2020Mendeley NIH | Mapping the Human Connect..., NWO | When growing tall is not ..., EC | PRISM +3 projectsNIH| Mapping the Human Connectome: Structure, Function, and Heritability ,NWO| When growing tall is not an option; down-regulation of shoot elongation in the shade ,EC| PRISM ,NWO| Language in Interaction ,WT| Integrated multimodal brain imaging for neuroscience research and clinical practice. ,WTAuthors: Guimaraes, Joao;Guimaraes, Joao;This directory contains the data and scripts used in the Methods & Materials of the study "Discovering the shared biology of cognitive traits determined by genetic overlap". It is split into 3 sections: - Twin Modeling: genetic analysis estimating shared genetics among cognitive traits (each individually driven by genetic factors, i.e. heritable). - Functional Imaging Meta-analysis: estimation of brain maps representative of cognitive activation across studies, individually for each cognitive trait reporting shared genetics - Brain Activation Overlap: quantify and infer overlap among cognitive maps, mimicking their respective genetic association, via three approaches: shared number of voxels, dice similarity coefficient, and the permuted correlation approach reported by Alexander-Bloch et al 2018
Mendeley Data; NARCI... arrow_drop_down Mendeley Data; NARCIS; DANS-EASYDataset . 2020add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert Mendeley Data; NARCI... arrow_drop_down Mendeley Data; NARCIS; DANS-EASYDataset . 2020add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020 Spainfigshare ANR | POSEIDON, EC | INMARE, ANR | OCEANOMICS +9 projectsANR| POSEIDON ,EC| INMARE ,ANR| OCEANOMICS ,EC| GROWCEAN ,WT ,ANR| HydroGen ,NSF| Ecological impacts and drivers of double-stranded DNA viral communities in the global oceans ,TARA| Tara Oceans ,ANR| Amidex ,NSF| Diversity of marine protists: single cell genomics and imaging for Tara Oceans ,NSF| Ecology and biogeochemical impacts of DNA and RNA viruses throughout the global oceans ,EC| MICRO B3Richter, Daniel J.; Watteaux, Romain; Vannier, Thomas; Leconte, Jade; Frémont, Paul; Reygondeau, Gabriel; Maillet, Nicolas; Henry, Nicolas; Benoit, Gaëtan; da Silva, Ophélie; Delmont, Tom O.; Fernández-Guerra, Antonio; Suweis, Samir; Narci, Romain; Berney, Cedric; Eveillard, Damien; Gavory, Frederick; Guidi, Lionel; Labadie, Karine; Mahieu, Eric; Poulain, Julie; Romac, Sarah; Roux, Simon; Dimier, Céline; Kandels‐Lewis, Stefanie; Picheral, Marc; Searson, Sarah; Oceans, Tara; Pesant, Stéphane; Aury, Jean‐Marc; Brum, Jennifer R.; Lemaitre, Claire; Pelletier, Eric; Bork, Peer; Sunagawa, Shinichi; Lombard, Fabien; Karp-Boss, Lee; Bowler, Chris; Sullivan, Matthew B.; Karsenti, Eric; Mariadassou, Mahendra; Probert, Ian; Peterlongo, Pierre; Wincker, Patrick; Vargas, Colomban de; Ribera d’Alcalà, Maurizio; Iudicone, Daniele; Jaillon, Olivier; Tara Oceans Coordinators;Supplementary Table 1. List of Tara Oceans samples sequenced with a metabarcoding (18S V9) approach and with a metagenomic approach, including identifiers for sequencing reads deposited in the DDBJ/ENA/GenBank Short Read Archives (SRA). [This Table is identical in version 2.] Supplementary Table 2. Table of environmental parameters for each sample. [This Table is identical in version 2.] Supplementary Table 3. Matrix of metagenomic dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.] Supplementary Table 4. Matrix of metagenomic dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.] Supplementary Table 5. Matrix of metagenomic dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.] Supplementary Table 6. Matrix of metagenomic dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.] Supplementary Table 7. Matrix of metagenomic dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.] Supplementary Table 8. Matrix of metagenomic dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.] Supplementary Table 9. Matrix of OTU dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.] Supplementary Table 10. Matrix of OTU dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.] Supplementary Table 11. Matrix of OTU dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.] Supplementary Table 12. Matrix of OTU dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.] Supplementary Table 13. Matrix of OTU dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.] Supplementary Table 14. Matrix of OTU dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.] Supplementary Table 15. Matrix of minimum travel time, in years. [This Table is identical in version 2.] Supplementary Table 16. Matrix of minimum geographic distance (without traversing land), in kilometers. [This Table is identical in version 2.] Supplementary Table 17. Matrix of imaging-based dissimilarity. [This Table is identical in version 2.] Supplementary Table 18. Matrix of metagenome-assembled genome (MAG)-based dissimilarity for the 20-180 μm size fraction. [The filename of this Table was modified from version 2. The contents of the Table are identical.] Supplementary Table 19. The cophenetic correlation coefficient for different methods of clustering metagenomic dissimilarity. [This Table is identical in version 2.] Supplementary Table 20. Baker's Gamma index comparing clustering results within size fractions. [This Table is identical in version 2.] Supplementary Table 21. Rand Index for K-means and spectral clustering, and multivariate ANOVA calculated by the adonis function. [This Table is identical in version 2.] Dataset 1. Reference database (in FASTA format) used to perform taxonomic assignment of metabarcodes. The header line of each reference V9 rDNA barcode (with a > sign) contains a unique identifier derived from GenBank accession number, followed by the taxonomic path associated to the reference barcode. [This Dataset is identical in version 2.] Dataset 2. V9 rDNA abundance at the metabarcode level. md5sum = unique identifier; totab = total abundance across all samples; cid = identifier of the OTU to which the barcode belongs (see Dataset 3); pid = best percentage identity to a barcode in Dataset 1; refs = identifier(s) of the best matching barcode(s) in Dataset 1; lineage = taxononmic lineage of the best match in Dataset 1; taxogroup = high-level taxonomic grouping of the best match in Dataset 1; sequence = V9 rDNA sequence; TV9_XXX = barcode abundance by sample (see Supplementary Table 1 for sample identifiers). [This Dataset is identical in version 2.] Dataset 3. V9 rDNA abundance at the OTU (operational taxonomic unit) level. cid = identifier of the OTU; md5sum = unique identifier of the most abundant barcode in the OTU; pid, refs, lineage, taxogroup, sequence = defined as in Dataset 2; rtotab = total abundance of the most abundant barcode in the OTU; ctotab = total abundance of all barcodes in the OTU; TV9_XXX = abundance by sample of all barcodes in the OTU (see Supplementary Table 1 for sample identifiers). [This Dataset is identical in version 2.] Dataset 4. Relative abundances of metagenome-assembled genomes (MAGs) in metagenomic samples from the 20-180 μm size fraction. [This Dataset is new in version 3.] Biogeographical studies have traditionally focused on readily visible organisms, but recent technological advances are enabling analyses of the large-scale distribution of microscopic organisms, whose biogeographical patterns have long been debated. Here we assessed the global structure of plankton geography and its relation to the biological, chemical and physical context of the ocean (the 'seascape') by analyzing metagenomes of plankton communities sampled across oceans during the Tara Oceans expedition, in light of environmental data and ocean current transport. Using a consistent approach across organismal sizes that provides unprecedented resolution to measure changes in genomic composition between communities, we report a pan-ocean, size-dependent plankton biogeography overlying regional heterogeneity. We found robust evidence for a basin-scale impact of transport by ocean currents on plankton biogeography, and on a characteristic timescale of community dynamics going beyond simple seasonality or life history transitions of plankton. Peer reviewed
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Research data keyboard_double_arrow_right Dataset 2022 EnglishPANGAEA EC | NEONANO, WT, EC | ZF-HEALTHEC| NEONANO ,WT ,EC| ZF-HEALTHAuthors: Leibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; +1 AuthorsLeibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; Michel, Maximilian;Obesity is a world wide problem and evidence suggests, that early lifetime undernourishment of caloric restirction predispose an organism for obesity and metabolic syndrome. We have raised two cohorts of zebrafish in an obesogenic environment (DIO) and compared several metabolic markers with fish raised under caloric restriction (CR) or fish shifted from CR to DIO at different periods in their life. We have looked morphologically at standard length and weight and found that fish on DIO grow faster in both axes. Fish shifted from CR to DIO show catch-up growth and not compensatory growth when shifted at one month, 3 months or 9 months of age. We have further characterized central agrp expression and hyperphagia, adipose tissue by histology as well as uCT imaging, hepatic histology, metabolic rate mitochondrial function as well as feeding induced glucose levels. We find that fish in an obesogenic environment develop markers of obesity which are not exacerbated by ealry lifetime food restriction.
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more_vert PANGAEA; PANGAEA - D... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 EnglishPANGAEA EC | NEONANO, EC | ZF-HEALTH, WTEC| NEONANO ,EC| ZF-HEALTH ,WTAuthors: Leibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; +1 AuthorsLeibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; Michel, Maximilian;For µCT imaging, adult zebrafish were fixed and decalcified in Bouin's solution at room temperature for 7 days, stored in PBS and imaged using a micro-computed tomography (µCT) device (SkyScan1272, Bruker BioSpin GmbH, Ettlingen, Germany). Zebrafish were placed individually in 1.5ml Eppendorf tubes using and an ultra-focus scan over the whole body was performed in a full-rotation in step-and-shoot mode. 322 projections (1008x672 pixels, 4x4 binning) were acquired per subscan with an x-ray tube voltage of 60 kV, power 0.166 mA, aluminum filter 0.25 mm,exposure time of 363 ms, 6 averages and a object-source distance of 86 mm. All CT images were reconstructed at an isotropic voxel size of 18 µm using a Feldkamp type algorithm (filtered back-projection). Fat-containing regions were appear hypo intense in µCT data and were segmented using Imalytics Preclinical (Gremse-IT GmbH, Aachen, Germany (Gremse et al., 2016; doi:10.7150/thno.13624). The volumetric fat percentage was calculated as the ratio of subcutaneous adipose tissue (SAT) or visceral adipose tissue (VAT) fat volume compared to the entire volume of the body cavity anterior of the anal fin and expressed per skeletal segment. Fish were raised as previously reported (Leibold and Hammerschmidt, 2015) for the following conditions:CG1: compensatory or catch up growth shifted at 1 month of ageCG3: compensatory or catch up growth shifted at 3 months of ageCG9: compensatory or catch up growth shifted at 9 months of ageCR: caloric restrictionDIO: diet induced obesityThe CT .nii files correlate to the groups as follows: Group 2: CG1; Group 3: DIO1; Group 6: CG3; Group 7 DIO3; Group 10: CG9; Group 11: DIO9; Group 1: CR1; Group 5: CR3; Group 9: CR9
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Embargo end date: 30 Nov 2021 EnglishDryad UKRI | Born in Bradford 2nd Wave, EC | LIFECYCLE, EC | ESCAPE +6 projectsUKRI| Born in Bradford 2nd Wave ,EC| LIFECYCLE ,EC| ESCAPE ,WT ,EC| CHICOS ,EC| HELIX ,EC| ENVIROGENOMARKERS ,EC| ATHLETE ,EC| ENRIECOAuthors: Ruiz-Arenas, Carlos; Bustamante, Mariona;Ruiz-Arenas, Carlos; Bustamante, Mariona;To test associations between DNA methylation levels and gene expression levels in cis (cis eQTMs), we paired each Gene to CpGs closer than 500 kb from its TSS, either upstream or downstream. For each Gene, the TSS was defined based on HTA-2.0 annotation, using the start position for transcripts in the + strand, and the end position for transcripts in the - strand. CpGs position was obtained from Illumina 450K array annotation. Only CpGs in autosomal chromosomes (from chromosome 1 to 22) were tested. In the main analysis, we fitted for each CpG-Gene pair a linear regression model between gene expression and methylation levels adjusted for age, sex, cohort, and blood cell type composition. A second model was run without adjusting for blood cellular composition and it is only reported on the online web catalog, but not discussed in this manuscript. Although some of the unique associations of the unadjusted model might be real, others might be confounded by the large methylation and expression changes among blood cell types. To ensure that CpGs paired to a higher number of Genes do not have higher chances of being part of an eQTM, multiple-testing was controlled at the CpG level, following a procedure previously applied in the Genotype-Tissue Expression (GTEx) project (Gamazon et al., 2018). Briefly, our statistic used to test the hypothesis that a pair CpG-Gene is significantly associated is based on considering the lowest p-value observed for a given CpG and all its paired Gene (e.g., those in the 1 Mb window centered at the TSS). As we do not know the distribution of this statistic under the null, we used a permutation test. We generated 100 permuted gene expression datasets and ran our previous linear regression models obtaining 100 permuted p-values for each CpG-Gene pair. Then, for each CpG, we selected among all CpG-Gene pairs the minimum p-value in each permutation and fitted a beta distribution that is the distribution we obtain when dealing with extreme values (e.g. minimum) (Dudbridge and Gusnanto, 2008). Next, for each CpG, we took the minimum p-value observed in the real data and used the beta distribution to compute the probability of observing a lower p-value. We defined this probability as the empirical p-value of the CpG. Then, we considered as significant those CpGs with empirical p-values to be significant at 5% false discovery rate using Benjamini-Hochberg method. Finally, we applied a last step to identify all significant CpG-Gene pairs for all eCpGs. To do so, we defined a genome-wide empirical p-value threshold as the empirical p-value of the eCpG closest to the 5% false discovery rate threshold. We used this empirical p-value to calculate a nominal p-value threshold for each eCpG, based on the beta distribution obtained from the minimum permuted p-values. This nominal p-value threshold was defined as the value for which the inverse cumulative distribution of the beta distribution was equal to the empirical p-value. Then, for each eCpG, we considered as significant all eCpG-Gene variants with a p-value smaller than nominal p-value. For the meQTLs catalogue, we selected 9.9 M cis and trans meQTLs with a p-value <1e-7 in the ARIES dataset consisting of data from children of 7 years old (Gaunt et al., 2016). Then, we tested whether this subset of 9.9 M SNPs were also meQTLs in HELIX by running meQTL analyses using MatrixEQTL R package (Shabalin, 2012), adjusting for cohort, sex, age, blood cellular composition and the first 20 principal components (PCs) calculated from genome-wide genetic data of the GWAS variability. We confirmed 2.8 M meQTLs in HELIX (p-value <1e-7). Trans meQTLs represented <10% of the 2.8 M meQTLs. Enrichment of eCpGs for meQTLs was computed using a Chi-square test, using non eCpGs as background. Finally, we tested whether meQTLs were also eQTLs for the eGenes linked to the eCpGs. To this end, we run eQTL analyses (gene expression being the outcome and 2.8 M SNPs the predictors) with MatrixEQTL adjusting for cohort, sex, age, blood cellular composition and the first 20 GWAS PCs in HELIX. We considered as significant eQTLs the SNP-Gene pairs with p-value <1e-7 and with the direction of the effect consistent with the direction of the meQTL and the eQTM. Background: The identification of expression quantitative trait methylation (eQTMs), defined as associations between DNA methylation levels and gene expression, might help the biological interpretation of epigenome-wide association studies (EWAS). We aimed to identify autosomal cis eQTMs in children’s blood, using data from 832 children of the Human Early Life Exposome (HELIX) project. Methods: Blood DNA methylation and gene expression were measured with the Illumina 450K and the Affymetrix HTA v2 arrays, respectively. The relationship between methylation levels and expression of nearby genes (1 Mb window centered at the transcription start site, TSS) was assessed by fitting 13.6 M linear regressions adjusting for sex, age, cohort, and blood cell composition. Results: We identified 39,749 blood autosomal cis eQTMs, representing 21,966 unique CpGs (eCpGs, 5.7% of total CpGs) and 8,886 unique transcript clusters (eGenes, 15.3% of total transcript clusters, equivalent to genes). In 87.9% of these cis eQTMs, the eCpG was located at <250 kb from eGene’s TSS; and 58.8% of all eQTMs showed an inverse relationship between the methylation and expression levels. Only around half of the autosomal cis-eQTMs eGenes could be captured through annotation of the eCpG to the closest gene. eCpGs had less measurement error and were enriched for active blood regulatory regions and for CpGs reported to be associated with environmental exposures or phenotypic traits. 40.4% of eQTMs had at least one genetic variant associated with methylation and expression levels. The overlap of autosomal cis eQTMs in children’s blood with those described in adults was small (13.8%), and age-shared cis eQTMs tended to be proximal to the TSS and enriched for genetic variants. See HELIX_Blood_eQTM_READMEfile_20210205.xlsx.
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