Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.

BackgroundGestational age is often used as a proxy for developmental maturity by clinicians and researchers alike. DNA methylation has previously been shown to be associated with age and has been used to accurately estimate chronological age in children and adults. In the current study, we examine whether DNA methylation in cord blood can be used to estimate gestational age at birth.ResultsWe find that gestational age can be accurately estimated from DNA methylation of neonatal cord blood and blood spot samples. We calculate a DNA methylation gestational age using 148 CpG sites selected through elastic net regression in six training datasets. We evaluate predictive accuracy in nine testing datasets and find that the accuracy of the DNA methylation gestational age is consistent with that of gestational age estimates based on established methods, such as ultrasound. We also find that an increased DNA methylation gestational age relative to clinical gestational age is associated with birthweight independent of gestational age, sex, and ancestry.ConclusionsDNA methylation can be used to accurately estimate gestational age at or near birth and may provide additional information relevant to developmental stage. Further studies of this predictor are warranted to determine its utility in clinical settings and for research purposes. When clinical estimates are available this measure may increase accuracy in the testing of hypotheses related to developmental age and other early life circumstances.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1068-z) contains supplementary material, which is available to authorized users.

Abstract. The early Eocene (56 to 48 million years ago) is inferred to have been the most recent time that Earth's atmospheric CO2 concentrations exceeded 1000 ppm. Global mean temperatures were also substantially warmer than those of the present day. As such, the study of early Eocene climate provides insight into how a super-warm Earth system behaves and offers an opportunity to evaluate climate models under conditions of high greenhouse gas forcing. The Deep Time Model Intercomparison Project (DeepMIP) is a systematic model–model and model–data intercomparison of three early Paleogene time slices: latest Paleocene, Paleocene–Eocene thermal maximum (PETM) and early Eocene climatic optimum (EECO). A previous article outlined the model experimental design for climate model simulations. In this article, we outline the methodologies to be used for the compilation and analysis of climate proxy data, primarily proxies for temperature and CO2. This paper establishes the protocols for a concerted and coordinated effort to compile the climate proxy records across a wide geographic range. The resulting climate “atlas” will be used to constrain and evaluate climate models for the three selected time intervals and provide insights into the mechanisms that control these warm climate states. We provide version 0.1 of this database, in anticipation that this will be expanded in subsequent publications.

BackgroundGene expression can be influenced by DNA methylation 1) distally, at regulatory elements such as enhancers, as well as 2) proximally, at promoters. Our current understanding of the influence of distal DNA methylation changes on gene expression patterns is incomplete. Here, we characterize genome-wide methylation and expression patterns for ~ 13 k genes to explore how DNA methylation interacts with gene expression, throughout the genome.ResultsWe used a linear mixed model framework to assess the correlation of DNA methylation at ~ 400 k CpGs with gene expression changes at ~ 13 k transcripts in two independent datasets from human blood cells. Among CpGs at which methylation significantly associates with transcription (eCpGs), > 50% are distal (> 50 kb) or trans (different chromosome) to the correlated gene. Many eCpG-transcript pairs are consistent between studies and ~ 90% of neighboring eCpGs associate with the same gene, within studies. We find that enhancers (P < 5e-18) and microRNA genes (P = 9e-3) are overrepresented among trans eCpGs, and insulators and long intergenic non-coding RNAs are enriched among cis and distal eCpGs. Intragenic-eCpG-transcript correlations are negative in 60–70% of occurrences and are enriched for annotated gene promoters and enhancers (P < 0.002), highlighting the importance of intragenic regulation. Gene Ontology analysis indicates that trans eCpGs are enriched for transcription factor genes and chromatin modifiers, suggesting that some trans eCpGs represent the influence of gene networks and higher-order transcriptional control.ConclusionsThis work sheds new light on the interplay between epigenetic changes and gene expression, and provides useful data for mining biologically-relevant results from epigenome-wide association studies.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4842-3) contains supplementary material, which is available to authorized users.

Abstract. The early Eocene (56 to 48 million years ago) is inferred to have been the most recent time that Earth's atmospheric CO2 concentrations exceeded 1000 ppm. Global mean temperatures were also substantially warmer than present day. As such, study of early Eocene climate provides insight into how a super-warm Earth system behaves and offers an opportunity to evaluate climate models under conditions of high greenhouse gas forcing. The Deep Time Model Intercomparison Project (DeepMIP) is a systematic model-model and model-data intercomparison of three early Paleogne time slices: latest Paleocene, Paleocene-Eocene thermal maximum and early Eocene climatic optimum. A previous article outlined the model experimental design for climate model simulations. In this article, we outline the methodologies to be used for the compilation and analysis of climate proxy data, primarily proxies for temperature and CO2. This paper establishes the protocols for a concerted and coordinated effort to compile the climate proxy records across a wide geographic range. The resulting climate atlas will be used to constrain and evaluate climate models for the three selected time intervals, and provide insights into the mechanisms that control these warm climate states. We provide version 0.1 of this database, in anticipation that this will be expanded in subsequent publications.

The early Paleogene (56-48 Myr) provides valuable information about the Earth's climate system in an equilibrium high pCO2 world. High ocean temperatures have been reconstructed for this greenhouse period, but land temperature estimates have been cooler than expected. This mismatch between marine and terrestrial temperatures has been difficult to reconcile. Here we present terrestrial temperature estimates from a newly-calibrated brGDGTbased paleothermometer in ancient lignites (fossilized peat). Our results suggest early Paleogene mid-latitude (45-60 degrees paleolatitude) mean annual air temperatures of 23-29 °C (with an uncertainty of ± 4.7 °C), 5-10 °C higher than

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Human deep space and planetary travel is limited by uncertainties regarding the health risks associated with exposure to galactic cosmic radiation (GCR), and in particular the high linear energy transfer (LET), heavy ion component. Here we assessed the impact of two high-LET ions 56Fe and 28Si, and low-LET X rays on genome-wide methylation patterns in human bronchial epithelial cells. We found that all three radiation types induced rapid and stable changes in DNA methylation but at distinct subsets of CpG sites affecting different chromatin compartments. The 56Fe ions induced mostly hypermethylation, and primarily affected sites in open chromatin regions including enhancers, promoters and the edges (“shores”) of CpG islands. The 28Si ion-exposure had mixed effects, inducing both hyper and hypomethylation and affecting sites in more repressed heterochromatic environments, whereas X rays induced mostly hypomethylation, primarily at sites in gene bodies and intergenic regions. Significantly, the methylation status of 56Fe ion sensitive sites, but not those affected by X ray or 28Si ions, discriminated tumor from normal tissue for human lung adenocarcinomas and squamous cell carcinomas. Thus, high-LET radiation exposure leaves a lasting imprint on the epigenome, and affects sites relevant to human lung cancer. These methylation signatures may prove useful in monitoring the cumulative biological impact and associated cancer risks encountered by astronauts in deep space.