TY - JOUR
T1 - Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning
AU - Akagi, Takashi
AU - Masuda, Kanae
AU - Kuwada, Eriko
AU - Takeshita, Kouki
AU - Kawakatsu, Taiji
AU - Ariizumi, Tohru
AU - Kubo, Yasutaka
AU - Ushijima, Koichiro
AU - Uchida, Seiichi
N1 - Funding Information:
This work was supported by PRESTO from Japan Science and Technology Agency (JST) [JPMJPR20D1] to T.Ak., and Grant-in-Aid for JSPS Fellows for [19J23361] to K.M, JSPS Grant-in-Aid for Scientific Research on Innovative Areas from JSPS [19H04862] to T.Ak., and [JP16H06280] to S.U.
Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press on behalf of American Society of Plant Biologists.
PY - 2022/6
Y1 - 2022/6
N2 - 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.
AB - 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.
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U2 - 10.1093/plcell/koac079
DO - 10.1093/plcell/koac079
M3 - Article
C2 - 35258588
AN - SCOPUS:85131106159
VL - 34
SP - 2174
EP - 2187
JO - Plant Cell
JF - Plant Cell
SN - 1040-4651
IS - 6
ER -