Learning the regulatory grammar of DNA for gene expression engineering

The DNA regulatory code that governs gene expression is present in the gene regulatory structure that spans the coding and adjacent non-coding regulatory DNA regions, including promoters, terminators and untranslated regions. Deciphering this regulatory code, as well as how the whole gene regulatory structure interacts to produce mRNA transcripts and regulate mRNA abundance, can greatly improve our capabilities for controlling gene expression and solving problems related to both medicine and biotechnology. Here, we consider that natural systems offer the most accurate information on gene expression regulation and apply deep learning on over 20,000 mRNA datasets to learn the DNA-encoded regulatory code across a variety of model organisms from bacteria to Human ( https://www.nature.com/articles/s41467-020-19921-4 ). Since up to 82% of the regulatory code is encoded in the gene regulatory structure, mRNA abundance can be predicted directly from DNA with high accuracy in all model organisms. Coding and regulatory regions in fact carry both overlapping and orthogonal information and additively contribute to gene expression levels. By mining the gene expression models for the relevant DNA regulatory motifs, we uncover motif interactions across the whole gene regulatory structure that define over 3 orders of magnitude of gene expression levels. Based on these findings we develop a novel AI-guided approach for protein expression engineering and experimentally verify its usefulness. Our results challenge the current paradigm that single motifs or regulatory regions are solely responsible for gene expression levels. Instead, we demonstrate that the whole gene regulatory structure, comprising the DNA regulatory grammar of interacting DNA motifs across protein coding and adjacent regulatory regions, forms a coevolved transcriptional regulatory unit and provides a mechanism by which whole gene systems with pre-specified expression patterns can be designed.