BACKGROUND: Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor's methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples. RESULTS: To cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment.CONCLUSIONS: MultiDataSet is a suitable class for data integration under R and Bioconductor framework. ; This work has been partly funded by the Spanish Ministry of Economy and Competitiveness (MTM2015-68140-R). CH-F was supported by a grant from European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 308333 – the HELIX project. CR-A was supported by a FI fellowship from Catalan Government (#016FI_B 00272)
BACKGROUND: Prenatal exposure to air pollution has been associated with childhood respiratory disease and other adverse outcomes. Epigenetics is a suggested link between exposures and health outcomes. OBJECTIVES: We aimed to investigate associations between prenatal exposure to particulate matter (PM) with diameter [Formula: see text] ([Formula: see text]) or [Formula: see text] ([Formula: see text]) and DNA methylation in newborns and children. METHODS: We meta-analyzed associations between exposure to [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]) at maternal home addresses during pregnancy and newborn DNA methylation assessed by Illumina Infinium HumanMethylation450K BeadChip in nine European and American studies, with replication in 688 independent newborns and look-up analyses in 2,118 older children. We used two approaches, one focusing on single cytosine-phosphate-guanine (CpG) sites and another on differentially methylated regions (DMRs). We also related PM exposures to blood mRNA expression. RESULTS: Six CpGs were significantly associated [false discovery rate (FDR) [Formula: see text]] with prenatal [Formula: see text] and 14 with [Formula: see text] exposure. Two of the [Formula: see text] CpGs mapped to FAM13A (cg00905156) and NOTCH4 (cg06849931) previously associated with lung function and asthma. Although these associations did not replicate in the smaller newborn sample, both CpGs were significant ([Formula: see text]) in 7- to 9-y-olds. For cg06849931, however, the direction of the association was inconsistent. Concurrent [Formula: see text] exposure was associated with a significantly higher NOTCH4 expression at age 16 y. We also identified several DMRs associated with either prenatal [Formula: see text] and or [Formula: see text] exposure, of which two [Formula: see text] DMRs, including H19 and MARCH11, replicated in newborns. CONCLUSIONS: Several differentially methylated CpGs and DMRs associated with prenatal PM exposure were identified in newborns, with annotation to genes previously implicated in lung-related outcomes. https://doi.org/10.1289/EHP4522. ; The UK Medical Research Council and the Wellcome Trust (Grant ref. 102215/2/13/2) provides core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). This research was specifically funded by a joint grant from the UK Economic & Social and Biotechnology & Biological Sciences Research Councils (Grant ref. ES/N000498/1). ALSPAC was funded by the BBSRC (BBI025751/1 and BB/I025263/1). Air pollution exposure assessment was funded by Public Health England as part of the MRC-PHE Centre for Environment and Health, funded also by the UK Medical Research Council (Grant ref. MR/L01341X/1). BAMSE was supported by a European Union (grant agreement No. 261357), Swedish Foundation for Strategic Research (SSF) (RBc08-0027). E.M. is supported by a grant from the European Research Council under the European Union (EU) Horizon 2020 (H2020) research and innovation programme (grant agreement number 757919, TRIBALCHS: This work was supported by NIEHS grants K01ES017801, R01ES022216, and P30ES007048. EARLI was supported by NIH grants R01ES016443, R01ES023780, and R01ES017646 as well as by Autism Speaks (AS 5938). The ENVIRONAGE birth cohort is funded by the European Research Counsil (ERC-2012-StG.310898) and by funds of the Flemisch Scientific Research Council (FWO, N1516112/G.0.873.11N.10). The methylation assays were funded by the European Community's Seventh Framework Programme FP7/2007-2013 project EXPOsOMICS (grant no. 308610). Z.H. is supported by the Exposomics EC FP7 grant (Grant agreement no. 308610). ZH and A.G. and the Epigenetics Group at IARC are supported by grants from the Institut National du Cancer (INCa, Plan Cancer-EVA-Inserm, France) and Association pour la Recherche sur le Cancer (ARC, France). The EWAS data was funded by a grant to VWJ from Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA; project no. 050-060-810), by funds from the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC. V.W.J. also received a grant from Netherlands Organization for Health Research and Development (VIDI 016.136.361) and a Consolidator Grant from the European Research Council (ERC-2014-CoG-648916). J.F.F. has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 633595 (DynaHEALTH). This project received funding from the European Union's Horizon 2020 Research and Innovation Programme (733206, LIFECYCLE). HELIX was supported by funding from the European Community's Seventh Framework Programme (FP7/2007-206) under grant agreement no 308333 – the HELIX project. R.G. received the grant of the Lithuanian Agency for Science Innovation and Technology (No. 45 31V-66). The Norwegian Mother and Child Cohort Study (MoBa) is supported by the Ministry of Health and Care Services and the Ministry of Education and Research, NIH/NIEHS (contract no. N01-ES-75558), NIH/NINDS (grant no. 1 UO1 NS 047537-01 and grant no. 2 UO1 NS 047537-06A1). INMA was funded by grants from Instituto de Salud Carlos III (Red INMA G03/176), Generalitat de Catalunya-CIRIT 1999SGR 00241, and EU Commission (261357; 211250; 268479). Piccolipiù was funded by the Italian National Centre for Disease Prevention and Control (CCM grant 2010). The methylation assays were funded by the European Community's Seventh Framework Programme FP7/2007–2013 project EXPOsOMICS (grant no. 308610). Z.H. is supported by the Exposomics EC FP7 grant (Grant agreement no: 308610). Z.H. and A.G. and the Epigenetics Group at IARC are supported by grants from the Institut National du Cancer (INCa, Plan Cancer-EVA-INSERM, France) and Association pour la Recherche sur le Cancer (ARC, France). Rhea: The methylation assays were funded by the European Community's Seventh Framework Programme FP7/2007-2013 project EXPOsOMICS (grant no. 308610). Z.H. is supported by the Exposomics EC FP7 grant (grant agreement no. 308610). ZH and A.G. and the Epigenetics Group at IARC are supported by grants from the Institut National du Cancer (INCa, Plan Cancer-EVA-INSERM, France) and Association pour la Recherche sur le Cancer (ARC, France). PRISM: R.J.W. received funding for the PRISM cohort under R01 HL095606 and R01 HL1143396. A.C.J. is supported by R00 ES023450. Project Viva was supported by grants from the NIH (NIH R01 HL 111108, R01 NR013945, R01 HD 034568, K24 HD069408, K23 ES022242, P01ES009825, R01AI102960, P30 ES000002) and the U.S. Environmental Protection Agency (EPA) (R832416, RD834798). MeDALL: The methylation study of MeDALL cohorts was funded by MEDALL, a collaborative project supported by the European Union under the Health Cooperation Work Programme of the 7th Framework Programme (grant agreement no. 261357). The Biobank-Based Integrative Omics Studies (BIOS) Consortium is funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007). BAMSE: was supported by the Project b2014110
BACKGROUND: Terrestrial ultraviolet (UV) radiation causes erythema, oxidative stress, DNA mutations and skin cancer. Skin can adapt to these adverse effects by DNA repair, apoptosis, keratinization and tanning. OBJECTIVES: To investigate the transcriptional response to fluorescent solar-simulated radiation (FSSR) in sun-sensitive human skin in vivo. METHODS: Seven healthy male volunteers were exposed to 0, 3 and 6 standard erythemal doses (SED). Skin biopsies were taken at 6 h and 24 h after exposure. Gene and microRNA expression were quantified with next generation sequencing. A set of candidate genes was validated by quantitative polymerase chain reaction (qPCR); and wavelength dependence was examined in other volunteers through microarrays. RESULTS: The number of differentially expressed genes increased with FSSR dose and decreased between 6 and 24 h. Six hours after 6 SED, 4071 genes were differentially expressed, but only 16 genes were affected at 24 h after 3 SED. Genes for apoptosis and keratinization were prominent at 6 h, whereas inflammation and immunoregulation genes were predominant at 24 h. Validation by qPCR confirmed the altered expression of nine genes detected under all conditions; genes related to DNA repair and apoptosis; immunity and inflammation; pigmentation; and vitamin D synthesis. In general, candidate genes also responded to UVA1 (340-400 nm) and/or UVB (300 nm), but with variations in wavelength dependence and peak expression time. Only four microRNAs were differentially expressed by FSSR. CONCLUSIONS: The UV radiation doses of this acute study are readily achieved daily during holidays in the sun, suggesting that the skin transcriptional profile of 'typical' holiday makers is markedly deregulated. What's already known about this topic? The skin's transcriptional profile underpins its adverse (i.e. inflammation) and adaptive molecular, cellular and clinical responses (i.e. tanning, hyperkeratosis) to solar ultraviolet radiation. Few studies have assessed microRNA and gene expression in vivo in humans, and there is a lack of information on dose, time and waveband effects. What does this study add? Acute doses of fluorescent solar-simulated radiation (FSSR), of similar magnitude to those received daily in holiday situations, markedly altered the skin's transcriptional profiles. The number of differentially expressed genes was FSSR-dose-dependent, reached a peak at 6 h and returned to baseline at 24 h. The initial transcriptional response involved apoptosis and keratinization, followed by inflammation and immune modulation. In these conditions, microRNA expression was less affected than gene expression. ; This study was supported by CERCA Programme/Generalitat de Catalunya and it was funded by the AGAUR with the support of Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya (2017 SGR 919), the Instituto de Salud Carlos III (PI10/02235 and PI17/01225, the European Union (FEDER), "Una manera de hacer Europa"), the Spanish Ministry of Economy and Competitiveness (MTM2015‐68140‐R), the European Commission, under the Framework 7 Programme Environment Theme [Contract No. 227020: The Impact of Climate and Environmental Factors on Personal Ultraviolet Radiation Exposure and Human Health (ICEPURE)] and the U.K. National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London, London, U.K. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the U.K. Department of Health. W.K. and M.R.F. acknowledge funding from the Strategic Research Area programme of the Swedish Research Council through Stockholm University. La Roche‐Posay provided funding for the microarray processing that was done by Milteyni Biotec GmbH (Bergisch Gladbach, Germany). AGAUR (2017 SGR 919).
Background: Environmental exposures in early life influence the development of behavioral outcomes in children, but research has not considered multiple exposures. We therefore aimed to investigate the impact of a broad spectrum of pre- and postnatal environmental exposures on child behavior. Methods and findings: We used data from the HELIX (Human Early Life Exposome) project, which was based on six longitudinal population-based birth cohorts in Europe. At 6-11 years, children underwent a follow-up to characterize their exposures and assess behavioral problems. We measured 88 prenatal and 123 childhood environmental factors, including outdoor, indoor, chemical, lifestyle and social exposures. Parent-reported behavioral problems included (1) internalizing, (2) externalizing scores, using the child behavior checklist (CBCL), and (3) the Conner's Attention Deficit Hyperactivity Disorder (ADHD) index, all outcomes being discrete raw counts. We applied LASSO penalized negative binomial regression models to identify which exposures were associated with the outcomes, while adjusting for co-exposures. In the 1287 children (mean age 8.0 years), 7.3% had a neuropsychiatric medical diagnosis according to parent's reports. During pregnancy, smoking and car traffic showing the strongest associations (e.g. smoking with ADHD index, aMR:1.31 [1.09; 1.59]) among the 13 exposures selected by LASSO, for at least one of the outcomes. During childhood, longer sleep duration, healthy diet and higher family social capital were associated with reduced scores whereas higher exposure to lead, copper, indoor air pollution, unhealthy diet were associated with increased scores. Unexpected decreases in behavioral scores were found with polychlorinated biphenyls (PCBs) and organophosphate (OP) pesticides. Conclusions: Our systematic exposome approach identified several environmental contaminants and healthy lifestyle habits that may influence behavioral problems in children. Modifying environmental exposures early in life may limit lifetime mental health risk. ; The LIFE-CYCLE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 733206.
Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes. ; We acknowledge the support of the developers of PhenoTips, which was used in the past by RD-Connect and NeurOmics as the primary tool to collate phenotypic data. We would also like to thank the leaders and members of the Instituto Nacional de Bioinformática (INB) and ELIXIR for their support and collaboration throughout the years. RD-Connect (RD-Connect, an integrated platform connecting registries, biobanks, and clinical bioinformatics) received funding from the Seventh Framework (FP7) Programme of the European Union under grant agreement No 305444. Data were analyzed using the RD-Connect GPAP, which received funding from EU ...
