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Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.
Thursday, 2022/08/18 | 08:22:14

Akagi T, Masuda K, Kuwada E, Takeshita K, Kawakatsu T, Ariizumi T, Kubo Y, Ushijima K, Uchida S.

Plant Cell; 2022 May 24; 34(6):2174-2187. doi: 10.1093/plcell/koac079.

Abstract

In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expression behaviors from CRE patterns to properly harness them, mainly because the biological processes are complex. In this study, we used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato (Solanum lycopersicum) fruit from the DNA sequences in gene regulatory regions. By fixing the effects of trans-acting factors using single cell-type spatiotemporal transcriptome data for the response variables, we developed a prediction model for crucial expression patterns in the initiation of tomato fruit ripening. Feature visualization of the CNNs identified nucleotide residues critical to the objective expression pattern in each gene, and their effects were validated experimentally in ripening tomato fruit. This cis-decoding framework will not only contribute to the understanding of the regulatory networks derived from CREs and transcription factor interactions, but also provides a flexible means of designing alleles for optimized expression.

 

See https://pubmed.ncbi.nlm.nih.gov/35258588/

Figure 6

Model for expression design based on explainable DL. If the objective expression patterns can be well predicted from CRE arrays, two-step feature visualization in the prediction models (or the second and then first DL models, see Figure 1B) will allow identification of the nucleotide-scale factor(s) responsible for the expression pattern. Randomization of the responsible residues can derive potentially unlimited variations for the objective expression pattern, which can be easily predicted using the first and second DL models. Once a desirable expression pattern is predicted, cis-editing with the CRISPR–Cas system may realize the design of the optimized allele.

 

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