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The following results are related to European Marine Science. Are you interested to view more results? Visit OpenAIRE - Explore.
16 Research products

  • European Marine Science
  • Research data
  • Other research products
  • 2019-2023
  • Wellcome Trust

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Leibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; +1 Authors

    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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    Authors: Leibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; +1 Authors

    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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    Authors: 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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    Authors: Georgakis, Marios; Gill, Dipender; Webb, Alastair; Evangelou, Evangelos; +6 Authors

    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

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    Authors: 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

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    Authors: Richter, Daniel J.; Watteaux, Romain; Vannier, Thomas; Leconte, Jade; +45 Authors

    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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    DIGITAL.CSIC
    Dataset . 2021
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      DIGITAL.CSIC
      Dataset . 2021
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    Authors: Marron, Alan; Cassarino, Lucie; Hatton, Jade; Curnow, Paul; +1 Authors

    The marine silicon cycle is intrinsically linked with carbon cycling in the oceans via biological production of silica by a wide range of organisms. The stable silicon isotopic composition (denoted by δ30Si) of siliceous microfossils extracted from sediment cores can be used as an archive of past oceanic silicon cycling. However, the silicon isotopic composition of biogenic silica has only been measured in diatoms, sponges and radiolarians, and isotopic fractionation relative to seawater is entirely unknown for many other silicifiers. Furthermore, the biochemical pathways and mechanisms that determine isotopic fractionation during biosilicification remain poorly understood. Here, we present the first measurements of the silicon isotopic fractionation during biosilicification by loricate choanoflagellates, a group of protists closely related to animals. We cultured two species of choanoflagellates, Diaphanoeca grandis and Stephanoeca diplocostata, which showed consistently greater isotopic fractionation (approximately −5 ‰ to −7 ‰) than cultured diatoms (−0.5 ‰ to −2.1 ‰). Instead, choanoflagellate silicon isotopic fractionation appears to be more similar to sponges grown under similar dissolved silica concentrations. Our results highlight that there is a taxonomic component to silicon isotope fractionation during biosilicification, possibly via a shared or related biochemical transport pathway. These findings have implications for the use of biogenic silica δ30Si produced by different silicifiers as proxies for past oceanic change.

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    Authors: Arrowsmith, Cheryl;

    Supporting information containing Spectroscopic and chromatography data 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

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    Authors: Cordero-Grande, Lucilio;

    Tools for complex DWI denoising using SVD shrinkage, adult and neonatal examples 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

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    Authors: Heskamp, Linda; Van Nimwegen, Marlies; Ploegmakers, Marieke J.; Bassez, Guillaume; +5 Authors

    Objective: To determine the value of quantitative MRI to provide imaging biomarkers for disease in 20 different upper and lower leg muscles of myotonic dystrophy type 1 (DM1) patients. Methods: We acquired images covering these muscles in 33 genetically and clinically well-characterized DM1 patients and 10 unaffected controls. MR images were recorded with a Dixon method to determine muscle fat fraction, muscle volume and contractile muscle volume, and a multi-spin echo sequence to determine T2 water relaxation time (T2water), reflecting putative oedema. Results: Muscles in DM1 patients had higher fat fractions than muscles of controls (15.6%±11.1% vs. 3.7%±1.5%). Also, patients had smaller muscle volumes (902±232 cm3 vs. 1097±251 cm3), contractile muscle volumes (779±247 cm3 vs. 1054±246 cm3), and increased T2water (33.4±1.0 ms vs. 31.9±0.6 ms), indicating atrophy and oedema, respectively. Lower leg muscles were affected most frequently, especially the gastrocnemius medialis and soleus. Distribution of fat content per muscle indicated gradual fat infiltration in DM1. Between-patient variation in fat fraction was explained by age (~45%), and another ~14% by estimated progenitor CTG repeat length (r2 = 0.485) and somatic instability (r2 = 0.590). Fat fraction correlated with the six-minute walk test (r = -0.553) and muscular impairment rating scale (r = 0.537), and revealed subclinical muscle involvement. Conclusion: This cross-sectional quantitative MRI study of 20 different lower extremity muscles in DM1 patients revealed abnormal values for muscle fat fraction, volume and T2water, which therefore may serve as objective biomarkers to assess disease state of skeletal muscles in these patients. Supplementary DataThe supplementary data file contains 1) A more detailed description of the applied T2water fitting approach to quantify putative oedema in the lower extremity muscles, 2) MRI outcome measures depicted for the 20 individual lower extremity muscles, 3) Detailed outcomes of the multivariate linear regression model used to assess the effect of age and CTG repeat length on lower extremity muscle fat fraction, 4) Effect of AciI-sensitive variant repeats in the expanded allele on lower extremity muscle fat fraction. 5) Detailed overview of the correlation between the 6MWT and the fat fraction in four functional muscle groups (quadriceps, hamstrings, anterior compartment of the lower leg, and triceps surae).Supplementary Material.pdf

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    Authors: Leibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; +1 Authors

    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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    Authors: Leibold, Sandra; Lakshminarasimha, Amrutha Bagivalu; Gremse, Felix; Hammerschmidt, Matthias; +1 Authors

    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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    Authors: 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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