Reliable detection of stochastic epigenetic mutations and associations with cardiovascular aging.

Stochastic epigenetic mutations (SEMs) have been proposed as novel aging biomarkers to capture heterogeneity in age-related DNA methylation changes. SEMs are defined as outlier methylation patterns at cytosine-guanine dinucleotide sites, categorized as hypermethylated (hyperSEM) or hypomethylated (hypoSEM) relative to a reference. Because SEMs are defined by their outlier status, it is critical to differentiate extreme values due to technical noise or data artifacts from those due to real biology. Using technical replicate data, we found SEM detection is not reliable: across 3 datasets, 24 to 39% of hypoSEM and 46 to 67% of hyperSEM are not shared between replicates. We identified factors influencing SEM reliability-including blood cell type composition, probe beta-value statistics, genomic location, and presence of SNPs. We used these factors in a training dataset to build a machine learning-based filter that removes unreliable SEMs, and found this filter enhances reliability in two independent validation datasets. We assessed associations between SEM loads and aging phenotypes in the Framingham Heart Study and discovered that associations with aging outcomes were in large part driven by hypoSEMs at baseline methylated probes and hyperSEMs at baseline unmethylated probes, which are the same subsets that demonstrate highest technical reliability. These aging associations were preserved after filtering out unreliable SEMs and were enhanced after adjusting for blood cell composition. Finally, we utilized these insights to formulate best practices for SEM detection and introduce a novel R package, SEMdetectR, which uses parallel programming for efficient SEM detection with comprehensive options for detection, filtering, and analysis.

Stochastic Epigenetic Mutations: Reliable Detection and Associations with Cardiovascular Aging.

Stochastic Epigenetic Mutations (SEMs) have been proposed as novel aging biomarkers that have the potential to capture heterogeneity in age-related DNA methylation (DNAme) changes. SEMs are defined as outlier methylation patterns at cytosine-guanine dinucleotide (CpG) sites, categorized as hypermethylated (hyperSEM) or hypomethylated (hypoSEM) relative to a reference. While individual SEMs are rarely consistent across subjects, the SEM load - the total number of SEMs - increases with age. However, given poor technical reliability of measurement for many DNA methylation sites, we posited that many outliers might represent technical noise. Our study of whole blood samples from 36 individuals, each measured twice, found that 23.3% of hypoSEM and 45.6% hyperSEM are not shared between replicates. This diminishes the reliability of SEM loads, where intraclass correlation coefficients are 0.96 for hypoSEM and 0.90 for hyperSEM. We linked SEM reliability to multiple factors, including blood cell type composition, probe beta-value statistics, and presence of SNPs. A machine learning approach, leveraging these factors, filtered unreliable SEMs, enhancing reliability in a separate dataset of technical replicates from 128 individuals. Analysis of the Framingham Heart Study confirmed previously reported SEM association with mortality and revealed novel connections to cardiovascular disease. We discover that associations with aging outcomes are primarily driven by hypoSEMs at baseline methylated probes and hyperSEMs at baseline unmethylated probes, which are the same subsets that demonstrate highest technical reliability. These aging associations are preserved after filtering out unreliable SEMs and are enhanced after adjusting for blood cell composition. Finally, we utilize these insights to formulate best practices for SEM detection and introduce a novel R package, SEMdetectR, which utilizes parallel programming for efficient SEM detection with comprehensive options for detection, filtering, and analysis.

Human Histone Interaction Networks: An Old Concept, New Trends.

To elucidate the properties of human histone interactions on the large scale, we perform a comprehensive mapping of human histone interaction networks by using data from structural, chemical cross-linking and various high-throughput studies. Histone interactomes derived from different data sources show limited overlap and complement each other. It inspires us to integrate these data into the combined histone global interaction network which includes 5308 proteins and 10,330 interactions. The analysis of topological properties of the human histone interactome reveals its scale free behavior and high modularity. Our study of histone binding interfaces uncovers a remarkably high number of residues involved in interactions between histones and non-histone proteins, 80-90% of residues in histones H3 and H4 have at least one binding partner. Two types of histone binding modes are detected: interfaces conserved in most histone variants and variant specific interfaces. Finally, different types of chromatin factors recognize histones in nucleosomes via distinct binding modes, and many of these interfaces utilize acidic patches among other sites. Interaction networks are available at https://github.com/Panchenko-Lab/Human-histone-interactome.

Data sets on human histone interaction networks.

Here, we present the data of human histone interactomes generated and analysed in the research article by Peng et al., 2020 [1]. The histone interactome data provide a comprehensive mapping of human histone/nucleosome interaction networks by using different data sources from the structural, chemical cross-linking, and high-throughput studies. The histone interactions are presented at different levels of granularity in networks, including protein, domain, and residue-levels. All human histone interactome Cytoscape session files are available at https://github.com/Panchenko-Lab/Human-histone-interactome.

Molecular recognition of nucleosomes by binding partners.

Nucleosomes represent the elementary units of chromatin packing and hubs in epigenetic signaling pathways. Across the chromatin and over the lifetime of the eukaryotic cell, nucleosomes experience a broad repertoire of alterations that affect their structure and binding with various chromatin factors. Dynamics of the histone core, nucleosomal and linker DNA, and intrinsic disorder of histone tails add further complexity to the nucleosome interaction landscape. In light of our understanding through the growing number of experimental and computational studies, we review the emerging patterns of molecular recognition of nucleosomes by their binding partners and assess the basic mechanisms of its regulation.