Chromatin is of major relevance for gene expression, cell division, and differentiation. Here, we determined the landscape of Arabidopsis thaliana chromatin states using 16 features, including DNA sequence, CG methylation, histone variants, and modifications. The combinatorial complexity of chromatin can be reduced to nine states that describe chromatin with high resolution and robustness. Each chromatin state has a strong propensity to associate with a subset of other states defining a discrete number of chromatin motifs. These topographical relationships revealed that an intergenic state, characterized by H3K27me3 and slightly enriched in activation marks, physically separates the canonical Polycomb chromatin and two heterochromatin states from the rest of the euchromatin domains. Genomic elements are distinguished by specific chromatin states: four states span genes from transcriptional start sites (TSS) to termination sites and two contain regulatory regions upstream of TSS. Polycomb regions and the rest of the euchromatin can be connected by two major chromatin paths. Sequential chromatin immunoprecipitation experiments demonstrated the occurrence of H3K27me3 and H3K4me3 in the same chromatin fiber, within a two to three nucleosome size range. Our data provide insight into the Arabidopsis genome topography and the establishment of gene expression patterns, specification of DNA replication origins, and definition of chromatin domains.

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Background: DNA methylation (DNAm) may contribute to processes that underlie associations between air pollution and poor health. Therefore, our objective was to evaluate associations between DNAm and ambient concentrations of particulate matter (PM) ≤2.5, ≤10, and 2.5–10 μm in diameter (PM 2.5 ; PM 10 ; PM 2.5–10 ). Methods: We conducted a methylome-wide association study among twelve cohort- and race/ethnicity-stratified subpopulations from the Women’s Health Initiative and the Atherosclerosis Risk in Communities study ( n = 8397; mean age: 61.5 years; 83% female; 45% African American; 9% Hispanic/Latino American). We averaged geocoded address-specific estimates of daily and monthly mean PM concentrations over 2, 7, 28, and 365 days and 1 and 12 months before exams at which we measured leukocyte DNAm in whole blood. We estimated subpopulation-specific, DNAm-PM associations at approximately 485,000 Cytosine-phosphate-Guanine (CpG) sites in multi-level, linear, mixed-effects models. We combined subpopulation- and site-specific estimates in fixed-effects, inverse variance-weighted meta-analyses, then for associations that exceeded methylome-wide significance and were not heterogeneous across subpopulations ( P < 1.0 × 10 −7 ; P Cochran’s Q > 0.10), we characterized associations using publicly accessible genomic databases and attempted replication in the Cooperative Health Research in the Region of Augsburg (KORA) study. Results: Analyses identified significant DNAm-PM associations at three CpG sites. Twenty-eight-day mean PM 10 was positively associated with DNAm at cg19004594 (chromosome 20; MATN4 ; P = 3.33 × 10 −8 ). One-month mean PM 10 and PM 2.5–10 were positively associated with DNAm at cg24102420 (chromosome 10; ARPP21 ; P = 5.84 × 10 −8 ) and inversely associated with DNAm at cg12124767 (chromosome 7; CFTR ; P = 9.86 × 10 −8 ). The PM-sensitive CpG sites mapped to neurological, pulmonary, endocrine, and cardiovascular disease-related genes, but DNAm at those sites was not associated with gene expression in blood cells and did not replicate in KORA. Conclusions: Ambient PM concentrations were associated with DNAm at genomic regions potentially related to poor health among racially, ethnically and environmentally diverse populations of U.S. women and men. Further investigation is warranted to uncover mechanisms through which PM-induced epigenomic changes may cause disease.

Background Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the interconnectivity of genes is essential to unravel the underlying biological networks. However, the focus of many studies is on the differential expression of individual genes or on co-expression analysis. Methods Going beyond analysis of one gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify interconnectivities among individual genes as a causal network. We utilized different machine learning techniques to extract information from the network and identify differential regulatory pattern between cases and controls. We used data from the Allen Brain Atlas for replication. Results Employing the integrative systems approach on the data from CommonMind Consortium showed that gene transcription is controlled by genetic variants proximal to the gene (cis-regulatory factors), and transcribed distal genes (trans-regulatory factors). We identified differential gene regulatory patterns in SCZ-cases versus controls and novel SCZ-associated genes that may play roles in the disorder since some of them are primary expressed in human brain. In addition, we observed genes known associated with SCZ are not likely (OR = 0.59) to have high impacts (degree > 3) on the network. Conclusions Causal networks could reveal underlying patterns and the role of genes individually and as a group. Establishing principles that govern relationships between genes provides a mechanistic understanding of the dysregulated gene transcription patterns in SCZ and creates more efficient experimental designs for further studies. This information cannot be obtained by studying a single gene at the time.