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Deciphering the mechanical code of the genome and epigenome

Abstract

Diverse DNA-deforming processes are impacted by the local mechanical and structural properties of DNA, which in turn depend on local sequence and epigenetic modifications. Deciphering this mechanical code (that is, this dependence) has been challenging due to the lack of high-throughput experimental methods. Here we present a comprehensive characterization of the mechanical code. Utilizing high-throughput measurements of DNA bendability via loop-seq, we quantitatively established how the occurrence and spatial distribution of dinucleotides, tetranucleotides and methylated CpG impact DNA bendability. We used our measurements to develop a physical model for the sequence and methylation dependence of DNA bendability. We validated the model by performing loop-seq on mouse genomic sequences around transcription start sites and CTCF-binding sites. We applied our model to test the predictions of all-atom molecular dynamics simulations and to demonstrate that sequence and epigenetic modifications can mechanically encode regulatory information in diverse contexts.

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Fig. 1: Sequence features that impact intrinsic cyclizability.
Fig. 2: DNA shape and CpG methylation impact intrinsic cyclizability.
Fig. 3: Predictive models for the sequence-dependence of intrinsic cyclizability.
Fig. 4: The impact of DNA mechanics in diverse in diverse contexts.

Data availability

All new sequencing data reported as part of this study are deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive under accession number PRJNA746342. All measurements of intrinsic cyclizabilities obtained from our earlier study27 were based on sequencing data that are deposited in the NCBI Sequence Read Archive under accession number PRJNA667271. The following datasets used in this study were downloaded from the NCBI Gene Expression Omnibus using the following accession numbers: GSE97290 (nucleosome occupancy data around +1 nucleosome dyads in S. cerevisiae); GSE46957 (nucleosome occupancy data around +1 nucleosome dyads in S. pombe); GSE69336 (nucleosome occupancy data in D. melanogaster around TSSs); GSE82127 (nucleosome occupancy in Mus musculus around TSSs and CTCF-binding sites); GSE11431 (location of CTCF-binding sites in mouse embryonic stem cells); GSE147927 (location of TSSs in S. cerevisiae); and GSE55199 (TSS locations in E. coli). S. cerevisiae (sacCer3), S. pombe (spo2), D. melanogaster (BDGP5/dm3) and M. musculus (mm9) genome sequences were downloaded from the University of California, Santa Cruz Genome Browser (https://genome.ucsc.edu/cgi-bin/hgGateway). The E. coli MG1655 genome was downloaded from the NCBI Nucleotide database (accession number NC_000913.2). The H. influenza genome was downloaded from the NCBI GenBank database (L42023.1). The sequence of the Ω4 region of the C. elegans genome was downloaded from the supplementary material of ref. 68. Supplementary Tables 1–21 provide the following data: sequences and intrinsic cyclizability values in all libraries on which loop-seq was performed either in this study or earlier27, all sequences and predicted intrinsic cyclizability values around all genomic loci where we applied our predictive models to predict intrinsic cyclizability and the values of all parameters that quantify the contribution of short sequence features and their distributions to intrinsic cyclizability, as obtained in this study. Source data are provided with this paper.

Code availability

Custom MATLAB codes developed as part of this study for predicting intrinsic cyclizability based on linear regression models or neural nets have been deposited in Zenodo89.

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Acknowledgements

A.B. and T.H. thank J. S. Song for suggestions and insights pertaining to developing the linear predictive models. This work was supported by the Royal Society (URF\R21\211659 and RF\ERE\210288 to A.B.), funding from Durham University (to A.B.), National Science Foundation grants PHY-1430124 and EFMA-1933303 (to T.H.), National Institutes of Health grant GM122569 (to T.H.), the European Union’s Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie grant agreement number 754510 (to J.P.A), the Spanish Ministry of Science (RTI2018-096704-B-100) and AGAUR, Generalitat de Catalunya, Grups de Reserca Consolidats 2017 SGR 1110 (to M.O.). T.H. is an investigator with the Howard Hughes Medical Institute. A.B. is a Royal Society University Research Fellow.

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A.B. and T.H. designed the research. A.B. performed the research, analyzed the data and built the predictive models. A.B. and T.H. wrote the paper. D.G.B. compiled the nucleosome occupancy data from various organisms. B.C. investigated the pairwise correlation among NN–NN dinucleotide pairs in highly loopable and rigid sequences. Z.Q. assisted with the library preparation loop-seq experiments pertaining to the random library. J.P.A. and M.O. related cyclizabilities to DNA shape parameters.

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Correspondence to Aakash Basu or Taekjip Ha.

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Basu, A., Bobrovnikov, D.G., Cieza, B. et al. Deciphering the mechanical code of the genome and epigenome. Nat Struct Mol Biol 29, 1178–1187 (2022). https://doi.org/10.1038/s41594-022-00877-6

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