Genome-wide variability in recombination activity is associated with meiotic chromatin organization.

Recombination enables reciprocal exchange of genomic information between parental chromosomes and successful segregation of homologous chromosomes during meiosis. Errors in this process lead to negative health outcomes, whereas variability in recombination rate affects genome evolution. In mammals, most crossovers occur in hotspots defined by PRDM9 motifs, although PRDM9 binding peaks are not all equally hot. We hypothesize that dynamic patterns of meiotic genome folding are linked to recombination activity. We apply an integrative bioinformatics approach to analyze how three-dimensional (3D) chromosomal organization during meiosis relates to rates of double-strand-break (DSB) and crossover (CO) formation at PRDM9 binding peaks. We show that active, spatially accessible genomic regions during meiotic prophase are associated with DSB-favored loci, which further adopt a transient locally active configuration in early prophase. Conversely, crossover formation is depleted among DSBs in spatially accessible regions during meiotic prophase, particularly within gene bodies. We also find evidence that active chromatin regions have smaller average loop sizes in mammalian meiosis. Collectively, these findings establish that differences in chromatin architecture along chromosomal axes are associated with variable recombination activity. We propose an updated framework describing how 3D organization of brush-loop chromosomes during meiosis may modulate recombination.

Predicting 3D genome folding from DNA sequence with Akita.

In interphase, the human genome sequence folds in three dimensions into a rich variety of locus-specific contact patterns. Cohesin and CTCF (CCCTC-binding factor) are key regulators; perturbing the levels of either greatly disrupts genome-wide folding as assayed by chromosome conformation capture methods. Still, how a given DNA sequence encodes a particular locus-specific folding pattern remains unknown. Here we present a convolutional neural network, Akita, that accurately predicts genome folding from DNA sequence alone. Representations learned by Akita underscore the importance of an orientation-specific grammar for CTCF binding sites. Akita learns predictive nucleotide-level features of genome folding, revealing effects of nucleotides beyond the core CTCF motif. Once trained, Akita enables rapid in silico predictions. Accounting for this, we demonstrate how Akita can be used to perform in silico saturation mutagenesis, interpret eQTLs, make predictions for structural variants and probe species-specific genome folding. Collectively, these results enable decoding genome function from sequence through structure.

Chromatin features constrain structural variation across evolutionary timescales.

The potential impact of structural variants includes not only the duplication or deletion of coding sequences, but also the perturbation of noncoding DNA regulatory elements and structural chromatin features, including topological domains (TADs). Structural variants disrupting TAD boundaries have been implicated both in cancer and developmental disease; this likely occurs via "enhancer hijacking," whereby removal of the TAD boundary exposes enhancers to new target transcription start sites (TSSs). With this functional role, we hypothesized that boundaries would display evidence for negative selection. Here we demonstrate that the chromatin landscape constrains structural variation both within healthy humans and across primate evolution. In contrast, in patients with developmental delay, variants occur remarkably uniformly across genomic features, suggesting a potentially broad role for enhancer hijacking in human disease.

High order chromatin architecture shapes the landscape of chromosomal alterations in cancer.

The accumulation of data on structural variation in cancer genomes provides an opportunity to better understand the mechanisms of genomic alterations and the forces of selection that act upon these alterations in cancer. Here we test evidence supporting the influence of two major forces, spatial chromosome structure and purifying (or negative) selection, on the landscape of somatic copy-number alterations (SCNAs) in cancer. Using a maximum likelihood approach, we compare SCNA maps and three-dimensional genome architecture as determined by genome-wide chromosome conformation capture (HiC) and described by the proposed fractal-globule model. This analysis suggests that the distribution of chromosomal alterations in cancer is spatially related to three-dimensional genomic architecture and that purifying selection, as well as positive selection, influences SCNAs during somatic evolution of cancer cells.