Z-flipon variants reveal the many roles of Z-DNA and Z-RNA in health and disease.

Identifying roles for Z-DNA remains challenging given their dynamic nature. Here, we perform genome-wide interrogation with the DNABERT transformer algorithm trained on experimentally identified Z-DNA forming sequences (Z-flipons). The algorithm yields large performance enhancements (F1 = 0.83) over existing approaches and implements computational mutagenesis to assess the effects of base substitution on Z-DNA formation. We show Z-flipons are enriched in promoters and telomeres, overlapping quantitative trait loci for RNA expression, RNA editing, splicing, and disease-associated variants. We cross-validate across a number of orthogonal databases and define BZ junction motifs. Surprisingly, many effects we delineate are likely mediated through Z-RNA formation. A shared Z-RNA motif is identified in SCARF2, SMAD1, and CACNA1 transcripts, whereas other motifs are present in noncoding RNAs. We provide evidence for a Z-RNA fold that promotes adaptive immunity through alternative splicing of KRAB domain zinc finger proteins. An analysis of OMIM and presumptive gnomAD loss-of-function datasets reveals an overlap of Z-flipons with disease-causing variants in 8.6% and 2.9% of Mendelian disease genes, respectively, greatly extending the range of phenotypes mapped to Z-flipons.

DeepZ: A Deep Learning Approach for Z-DNA Prediction.

Here we describe an approach that uses deep learning neural networks such as CNN and RNN to aggregate information from DNA sequence; physical, chemical, and structural properties of nucleotides; and omics data on histone modifications, methylation, chromatin accessibility, and transcription factor binding sites and data from other available NGS experiments. We explain how with the trained model one can perform whole-genome annotation of Z-DNA regions and feature importance analysis in order to define key determinants for functional Z-DNA regions.

ADAR1 masks the cancer immunotherapeutic promise of ZBP1-driven necroptosis.

Only a small proportion of patients with cancer show lasting responses to immune checkpoint blockade (ICB)-based monotherapies. The RNA-editing enzyme ADAR1 is an emerging determinant of resistance to ICB therapy and prevents ICB responsiveness by repressing immunogenic double-stranded RNAs (dsRNAs), such as those arising from the dysregulated expression of endogenous retroviral elements (EREs)1-4. These dsRNAs trigger an interferon-dependent antitumour response by activating A-form dsRNA (A-RNA)-sensing proteins such as MDA-5 and PKR5. Here we show that ADAR1 also prevents the accrual of endogenous Z-form dsRNA elements (Z-RNAs), which were enriched in the 3' untranslated regions of interferon-stimulated mRNAs. Depletion or mutation of ADAR1 resulted in Z-RNA accumulation and activation of the Z-RNA sensor ZBP1, which culminated in RIPK3-mediated necroptosis. As no clinically viable ADAR1 inhibitors currently exist, we searched for a compound that can override the requirement for ADAR1 inhibition and directly activate ZBP1. We identified a small molecule, the curaxin CBL0137, which potently activates ZBP1 by triggering Z-DNA formation in cells. CBL0137 induced ZBP1-dependent necroptosis in cancer-associated fibroblasts and reversed ICB unresponsiveness in mouse models of melanoma. Collectively, these results demonstrate that ADAR1 represses endogenous Z-RNAs and identifies ZBP1-mediated necroptosis as a new determinant of tumour immunogenicity masked by ADAR1. Therapeutic activation of ZBP1-induced necroptosis provides a readily translatable avenue for rekindling the immune responsiveness of ICB-resistant human cancers.

Deep learning approach for predicting functional Z-DNA regions using omics data.

Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not sufficient to explain emergence of Z-DNA at different genomic locations. Adding epigenetic and other functional genomic mark-ups to DNA sequence level can help revealing the functional Z-DNA sites. Here we take advantage of the deep learning approach that can analyze and extract information from large volumes of molecular biology data. We developed a machine learning approach DeepZ that aggregates information from genome-wide maps of epigenetic markers, transcription factor and RNA polymerase binding sites, and chromosome accessibility maps. With the developed model we not only verify the experimental Z-DNA predictions, but also generate the whole-genome annotation, introducing new possible Z-DNA regions, which have not yet been found in experiments and can be of interest to the researchers from various fields.