Chemical map-based prediction of nucleosome positioning using the Bioconductor package nuCpos

SWI/SNF-dependent genes are defined by their chromatin landscape

SWI/SNF complexes are evolutionarily conserved, ATP-dependent chromatin remodeling machines. Here, we characterize the features of SWI/SNF-dependent genes using BRM014, an inhibitor of the ATPase activity of the complexes. We find that SWI/SNF activity is required to maintain chromatin accessibility and nucleosome occupancy for most enhancers but not for most promoters. SWI/SNF activity is needed for expression of genes with low to medium levels of expression that have promoters with (1) low chromatin accessibility, (2) low levels of active histone marks, (3) high H3K4me1/H3K4me3 ratio, (4) low nucleosomal phasing, and (5) enrichment in TATA-box motifs. These promoters are mostly occupied by the canonical Brahma-related gene 1/Brahma-associated factor (BAF) complex. These genes are surrounded by SWI/SNF-dependent enhancers and mainly encode signal transduction, developmental, and cell identity genes (with almost no housekeeping genes). Machine-learning models trained with different chromatin characteristics of promoters and their surrounding regulatory regions indicate that the chromatin landscape is a determinant for establishing SWI/SNF dependency.

Comprehensive computational analysis of epigenetic descriptors affecting CRISPR-Cas9 off-target activity

BACKGROUND

A common issue in CRISPR-Cas9 genome editing is off-target activity, which prevents the widespread use of CRISPR-Cas9 in medical applications. Among other factors, primary chromatin structure and epigenetics may influence off-target activity.

METHODS

In this work, we utilize crisprSQL, an off-target database, to analyze the effect of 19 epigenetic descriptors on CRISPR-Cas9 off-target activity. Termed as 19 epigenetic features/scores, they consist of 6 experimental epigenetic and 13 computed nucleosome organization-related features. In terms of novel features, 15 of the epigenetic scores are newly considered. The 15 newly considered scores consist of 13 freshly computed nucleosome occupancy/positioning scores and 2 experimental features (MNase and DRIP). The other 4 existing scores are experimental features (CTCF, DNase I, H3K4me3, RRBS) commonly used in deep learning models for off-target activity prediction. For data curation, MNase was aggregated from existing experimental nucleosome occupancy data. Based on the sequence context information available in crisprSQL, we also computed nucleosome occupancy/positioning scores for off-target sites.

RESULTS

To investigate the relationship between the 19 epigenetic features and off-target activity, we first conducted Spearman and Pearson correlation analysis. Such analysis shows that some computed scores derived from training-based models and training-free algorithms outperform all experimental epigenetic features. Next, we evaluated the contribution of all epigenetic features in two successful machine/deep learning models which predict off-target activity. We found that some computed scores, unlike all 6 experimental features, significantly contribute to the predictions of both models. As a practical research contribution, we make the off-target dataset containing all 19 epigenetic features available to the research community.

CONCLUSIONS

Our comprehensive computational analysis helps the CRISPR-Cas9 community better understand the relationship between epigenetic features and CRISPR-Cas9 off-target activity.

Controlling gene expression with deep generative design of regulatory DNA

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.

The biogenesis and function of nucleosome arrays

Numerous chromatin remodeling enzymes position nucleosomes in eukaryotic cells. Aside from these factors, transcription, DNA sequence, and statistical positioning of nucleosomes also shape the nucleosome landscape. The precise contributions of these processes remain unclear due to their functional redundancy in vivo. By incisive genome engineering, we radically decreased their redundancy in Saccharomyces cerevisiae. The transcriptional machinery strongly disrupts evenly spaced nucleosomes. Proper nucleosome density and DNA sequence are critical for their biogenesis. The INO80 remodeling complex helps space nucleosomes in vivo and positions the first nucleosome over genes in an H2A.Z-independent fashion. INO80 requires its Arp8 subunit but unexpectedly not the Nhp10 module for spacing. Cells with irregularly spaced nucleosomes suffer from genotoxic stress including DNA damage, recombination and transpositions. We derive a model of the biogenesis of the nucleosome landscape and suggest that it evolved not only to regulate but also to protect the genome.