Dynamic regulation of Z-DNA in the mouse prefrontal cortex by the RNA-editing enzyme Adar1 is required for fear extinction

Marshall, Paul R.; Zhao, Qiongyi; Li, Xiang; Wei, Wei; Periyakaruppiah, Ambika; Zajaczkowski, Esmi L.; Leighton, Laura J.; Madugalle, Sachithrani U.; Basic, Dean; Wang, Ziqi; Yin, Jiayu; Liau, Wei-Siang; Gupte, Ankita; Walkley, Carl R.; Bredy, Timothy W.

doi:10.1038/s41593-020-0627-5

Cited by 21 publications

(15 citation statements)

References 51 publications

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“…Potential Z-DNA regions change conformation from B to Z in response to a signal, and then must change it back to B-form. Dynamic formation of Z-DNA in response to fear was observed, and Z-DNA serves as a fast regulator of gene expression levels 53 . Moreover this process is reversible in a short period of time, and level of Z-DNA in the locus of interest was reduced after fear extinction training.…”

Section: Discussionmentioning

confidence: 99%

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

Beknazarov

Jin

Poptsova

2020

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

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

Beknazarov

Jin

Poptsova

2020

Sci Rep

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“…The presence of p150 in both the cytosol and the nucleus is consistent with the nucleocytoplasmic shuttling of p150 [ 67 – 69 ]. Recent surveys of normal tissues show that p150 is mostly nuclear in murine thymus, spleen and prefrontal cortex [ 70 – 72 ]. Conversely, the cytoplasmic localization during stress of ADAR p110 isoform that lacks the Z-DNA binding domain, is not often referenced [ 73 ].…”

Section: Why Is the Science Of Non-b-dna Structures Difficult?mentioning

confidence: 99%

“…A recent study of the ADAR Zα regulation of fear extinction in mice found that 80% (97/122) of RNA editing sites overlap with Adar1 DNA binding, with an overrepresentation of SINE/LINE elements nearby. Both DNA binding and RNA editing were absent when a loss-of-function Zα (N175A/Y179A) mutant was tested in the in vivo assay [ 70 ]. This response also depends upon prion formation by the cytoplasmic polyadenylation element binding protein CPEB3 [ 103 ].…”

Section: Z-fliponsmentioning

confidence: 99%

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ALU non-B-DNA conformations, flipons, binary codes and evolution

Herbert¹

2020

R. Soc. open sci.

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ALUs contribute to genetic diversity by altering DNA's linear sequence through retrotransposition, recombination and repair. ALUs also have the potential to form alternative non-B-DNA conformations such as Z-DNA, triplexes and quadruplexes that alter the read-out of information from the genome. I suggest here these structures enable the rapid reprogramming of cellular pathways to offset DNA damage and regulate inflammation. The experimental data supporting this form of genetic encoding is presented. ALU sequence motifs that form non-B-DNA conformations under physiological conditions are called flipons. Flipons are binary switches. They are dissipative structures that trade energy for information. By efficiently targeting cellular machines to active genes, flipons expand the repertoire of RNAs compiled from a gene. Their action greatly increases the informational capacity of linearly encoded genomes. Flipons are programmable by epigenetic modification, synchronizing cellular events by altering both chromatin state and nucleosome phasing. Different classes of flipon exist. Z-flipons are based on Z-DNA and modify the transcripts compiled from a gene. T-flipons are based on triplexes and localize non-coding RNAs that direct the assembly of cellular machines. G-flipons are based on G-quadruplexes and sense DNA damage, then trigger the appropriate protective responses. Flipon conformation is dynamic, changing with context. When frozen in one state, flipons often cause disease. The propagation of flipons throughout the genome by ALU elements represents a novel evolutionary innovation that allows for rapid change. Each ALU insertion creates variability by extracting a different set of information from the neighbourhood in which it lands. By elaborating on already successful adaptations, the newly compiled transcripts work with the old to enhance survival. Systems that optimize flipon settings through learning can adapt faster than with other forms of evolution. They avoid the risk of relying on random and irreversible codon rewrites.

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“…Nonetheless, since experimental data supporting that retroelements are direct targets of the Zα domain are lacking in these studies, other possibilities should remain valid. In fact, another recent study showed that in the mouse prefrontal cortex, the Zα domain of ADAR1 recognizes Z-DNA in host genomes to control gene expression during fear learning ( 52 ).…”

Section: Future Perspectivesmentioning

confidence: 99%

How Z-DNA/RNA binding proteins shape homeostasis, inflammation, and immunity

Kim

2020

BMB Rep.

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The right-handed double-helical structure of DNA (B-DNA), which follows the Watson-Crick model, is the canonical form of DNA existing in normal physiological settings. Even though an alternative left-handed structure of DNA (Z-DNA) was discovered in the late 1970s, Z-form nucleic acid has not received much attention from biologists, because it is extremely unstable under physiological conditions, has an ill-defined mechanism of its formation, and has obscure biological functions. The debate about the physiological relevance of Z-DNA was settled only after a class of proteins was found to potentially recognize the Z-form architecture of DNA. Interestingly, these Z-DNA binding proteins can bind not only the left-handed form of DNA but also the equivalent structure of RNA (Z-RNA). The Z-DNA/RNA binding proteins present from viruses to humans function as important regulators of biological processes. In particular, the proteins ADAR1 and ZBP1 are currently being extensively re-evaluated in the field to understand potential roles of the noncanonical Z-conformation of nucleic acids in host immune responses and human disease. Despite a growing body of evidence supporting the biological importance of Z-DNA/RNA, there remain many unanswered principal questions, such as when Z-form nucleic acids arise and how they signal to downstream pathways. Understanding Z-DNA/RNA and the sensors in different pathophysiological conditions will widen our view on the regulation of immune responses and open a new door of opportunity to develop novel types of immunomodulatory therapeutic possibilities. [BMB Reports 2020; 53(9): 453-457]

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Dynamic regulation of Z-DNA in the mouse prefrontal cortex by the RNA-editing enzyme Adar1 is required for fear extinction

Cited by 21 publications

References 51 publications

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

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

ALU non-B-DNA conformations, flipons, binary codes and evolution

How Z-DNA/RNA binding proteins shape homeostasis, inflammation, and immunity

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