ABSTRACT
The ability to measure human aging from molecular profiles has practical implications in many fields, including disease prevention and treatment, forensics, and extension of life. Although chronological age has been linked to changes in DNA methylation, the methylome has not yet been used to measure and compare human aging rates. Here, we build a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101. This model measures the rate at which an individual's methylome ages, which we show is impacted by gender and genetic variants. We also show that differences in aging rates help explain epigenetic drift and are reflected in the transcriptome. Moreover, we show how our aging model is upheld in other human tissues and reveals an advanced aging rate in tumor tissue. Our model highlights specific components of the aging process and provides a quantitative readout for studying the role of methylation in age-related disease.
Subject(s)
Aging/genetics , DNA Methylation , Genome, Human , Adult , Aged , Aged, 80 and over , Epigenesis, Genetic , Female , Genome-Wide Association Study , Humans , Male , Middle Aged , Models, Genetic , Phenotype , Sequence Analysis, DNA , Transcriptome , Young AdultABSTRACT
In eukaryotes, many chromatin proteins together regulate gene expression. Chromatin proteins often direct the genomic binding pattern of other chromatin proteins, for example, by recruitment or competition mechanisms. The network of such targeting interactions in chromatin is complex and still poorly understood. Based on genome-wide binding maps, we constructed a Bayesian network model of the targeting interactions among a broad set of 43 chromatin components in Drosophila cells. This model predicts many novel functional relationships. For example, we found that the homologous proteins HP1 and HP1C each target the heterochromatin protein HP3 to distinct sets of genes in a competitive manner. We also discovered a central role for the remodeling factor Brahma in the targeting of several DNA-binding factors, including GAGA factor, JRA, and SU(VAR)3-7. Our network model provides a global view of the targeting interplay among dozens of chromatin components.
Subject(s)
Chromatin/metabolism , DNA-Binding Proteins/metabolism , Drosophila melanogaster/genetics , Gene Regulatory Networks , Metabolic Networks and Pathways , Animals , Bayes Theorem , Models, Biological , Protein Interaction MappingABSTRACT
Classic "position-effect" experiments repositioned genes near telomeres to demonstrate that the epigenetic landscape can dramatically alter gene expression. Here, we show that systematic gene knockout collections provide an exceptional resource for interrogating position effects, not only near telomeres but at every genetic locus. Because a single reporter gene replaces each deleted gene, interrogating this reporter provides a sensitive probe into different chromatin environments while controlling for genetic context. Using this approach, we find that, whereas systematic replacement of yeast genes with the kanMX marker does not perturb the chromatin landscape, chromatin differences associated with gene position account for 35% of kanMX activity. We observe distinct chromatin influences, including a Set2/Rpd3-mediated antagonistic interaction between histone H3 lysine 36 trimethylation and the Rap1 transcriptional activation site in kanMX. This interaction explains why some yeast genes have been resistant to deletion and allows successful generation of these deletion strains through the use of a modified transformation procedure. These findings demonstrate that chromatin regulation is not governed by a uniform "histone code" but by specific interactions between chromatin and genetic factors.