Toward learning the principles of plant gene regulation

Zrimec, Jan; Zelezniak, Aleksej; Gruden, Kristina

doi:10.1016/j.tplants.2022.08.010

Cited by 6 publications

(24 citation statements)

References 15 publications

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“…We have shown that gene expression in response to abiotic stress can be predicted based on known and putative regulatory motifs in the promoter of a gene. Previous research indicated that gene expression regulation spans both coding and noncoding regions of a gene ( Washburn et al., 2019 ; Zrimec et al., 2020 ; Zrimec et al., 2022 ). Meng and collaborators have demonstrated that the transcriptional response to cold stress can be accurately predicted using the nucleotide and dinucleotide content of various genomic regions, covering both coding and noncoding DNA ( Meng et al., 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…They orchestrate gene regulation by sequence-specific binding to TF binding sites (TFBSs) in noncoding regions of the DNA, also known as cis-regulatory elements (CREs), located upstream, in introns, or downstream of the gene body. Together with transcription cofactors and RNA-polymerase II, TFs are key components of gene regulatory networks, which describe the interactions between TFs and the target genes they regulate ( Zrimec et al., 2020 ; Zrimec et al., 2022 ). Identifying CREs in the noncoding DNA can help to identify functional TFBSs and further unravel the regulatory grammar of abiotic stress response.…”

Section: Introductionmentioning

confidence: 99%

“…Gene expression regulation is, however, not restricted to noncoding regions. Recently, it has been shown that coding regions should be part of the gene regulatory structure ( Zrimec et al., 2020 ; Zrimec et al., 2022 ). The complete gene sequence is in fact highly predictive of the level of expression ( Washburn et al., 2019 ; Zrimec et al., 2020 ; Meng et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Elucidating gene expression properties will affect our understanding of plant physiology and help improve crop productivity ( Zrimec et al., 2020 ; Jores et al., 2021 ). Predicting gene expression patterns is one of many biological applications enabled by advances in supervised machine learning (ML) ( Washburn et al., 2019 ; Zrimec et al., 2020 ; Meng et al., 2021 ; Moore et al., 2022 ; Zrimec et al., 2022 ). Broadly, it can be divided into classical ML and representation learning.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice

Smet,

Opdebeeck,

Vandepoele

2023

Front. Plant Sci.

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Plants have evolved various mechanisms to adapt to adverse environmental stresses, such as the modulation of gene expression. Expression of stress-responsive genes is controlled by specific regulators, including transcription factors (TFs), that bind to sequence-specific binding sites, representing key components of cis-regulatory elements and regulatory networks. Our understanding of the underlying regulatory code remains, however, incomplete. Recent studies have shown that, by training machine learning (ML) algorithms on genomic sequence features, it is possible to predict which genes will transcriptionally respond to a specific stress. By identifying the most important features for gene expression prediction, these trained ML models allow, in theory, to further elucidate the regulatory code underlying the transcriptional response to abiotic stress. Here, we trained random forest ML models to predict gene expression in rice (Oryza sativa) in response to heat or drought stress. Apart from thoroughly assessing model performance and robustness across various input training data, the importance of promoter and gene body sequence features to train ML models was evaluated. The use of enriched promoter oligomers, complementing known TF binding sites, allowed us to gain novel insights in DNA motifs contributing to the stress regulatory code. By comparing genomic feature importance scores for drought and heat stress over time, general and stress-specific genomic features contributing to the performance of the learned models and their temporal variation were identified. This study provides a solid foundation to build and interpret ML models accurately predicting transcriptional responses and enables novel insights in biological sequence features that are important for abiotic stress responses.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice

Smet,

Opdebeeck,

Vandepoele

2023

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Enhancers, silencers, insulators, and locus control regions are important distal cis -regulatory elements (CREs) that regulate gene expression in a position- and orientation-independent manner ( Schmitz et al., 2022 ). These regulatory elements, in conjunction with proximal promoter cis elements, mediate gene regulation in an orchestrated manner ( Zrimec et al., 2020 ; Zrimec et al., 2022a , 2022b ). Given the uncertainty surrounding the positions of distal CREs, we will consider synthetic promoters to be composed of core and proximal promoter regions, irrespective of distal elements ( Schmitz et al., 2022 ).…”

Section: Promoter Architecture In Plantsmentioning

confidence: 99%