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Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters

Nature Plants volume 7, pages 842–855 (2021)Cite this article

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Abstract

Targeted engineering of plant gene expression holds great promise for ensuring food security and for producing biopharmaceuticals in plants. However, this engineering requires thorough knowledge of cis-regulatory elements to precisely control either endogenous or introduced genes. To generate this knowledge, we used a massively parallel reporter assay to measure the activity of nearly complete sets of promoters from Arabidopsis, maize and sorghum. We demonstrate that core promoter elements—notably the TATA box—as well as promoter GC content and promoter-proximal transcription factor binding sites influence promoter strength. By performing the experiments in two assay systems, leaves of the dicot tobacco and protoplasts of the monocot maize, we detect species-specific differences in the contributions of GC content and transcription factors to promoter strength. Using these observations, we built computational models to predict promoter strength in both assay systems, allowing us to design highly active promoters comparable in activity to the viral 35S minimal promoter. Our results establish a promising experimental approach to optimize native promoter elements and generate synthetic ones with desirable features.

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Fig. 1: STARR-seq measures core promoter strength in tobacco leaves and maize protoplasts.
Fig. 2: Plant core promoters span a wide range of activity.
Fig. 3: GC content affects promoter strength in tobacco leaves.
Fig. 4: The TATA box is a key determinant of promoter strength.
Fig. 5: Enhancer responsiveness of promoters depends on the TATA box and GC content.
Fig. 6: Promoter strength can be modulated by light.
Fig. 7: Design and validation of synthetic promoters.
Fig. 8: Computational models can predict promoter strength and enable in silico evolution of plant promoters.

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Data availability

All sequencing results are deposited in the NCBI Sequence Read Archive under the BioProject accession PRJNA714258.

Code availability

The code used in this study is available on Github (https://github.com/tobjores/Synthetic-Promoter-Designs-Enabled-by-a-Comprehensive-Analysis-of-Plant-Core-Promoters).

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Acknowledgements

We thank A. Gutierrez Diaz and E. Grotewold for providing maize TSS data, and A. Gallavotti for providing maize B73 seeds. This work was supported by the National Science Foundation (RESEARCH-PGR grant no. 1748843 to E.S.B., S.F. and C.Q.), the German Research Foundation (DFG; fellowship no. 441540116 to T.J.) and the National Institutes of Health (T32 training grant no. HG000035 to J.T. and R01-GM079712 to C.Q. and J.T.C.).

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Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Tobias Jores, Jackson Tonnies, Josh T. Cuperus, Stanley Fields & Christine Queitsch

  2. Graduate Program in Biology, University of Washington, Seattle, WA, USA

    Jackson Tonnies

  3. Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA

    Travis Wrightsman & Edward S. Buckler

  4. Agricultural Research Service, United States Department of Agriculture, Ithaca, NY, USA

    Edward S. Buckler

  5. Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA

    Edward S. Buckler

  6. Department of Medicine, University of Washington, Seattle, WA, USA

    Stanley Fields

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Contributions

All authors conceived and interpreted experiments and wrote the article. T.J. and J.T. performed experiments. T.J. analysed the data and prepared the figures. T.J. and T.W. did the in silico modelling.

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Correspondence to Josh T. Cuperus, Stanley Fields or Christine Queitsch.

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Extended data

Extended Data Fig. 1 Promoter strength and in vivo expression levels of corresponding genes are not correlated.

a, Correlation (Pearson’s r) between the promoter strength and expression levels of the corresponding genes in the indicated species. Each boxplot (centre line, median; box limits, upper and lower quartiles; whiskers, 1.5 × interquartile range; points, outliers) represents the correlation for all individual tissue samples in the RNA-seq dataset (see Methods). The number of samples in the RNA-seq dataset is indicated at the bottom of the plot. b,c, Examples of the correlation between gene expression (Arabidopsis adult cotyledon (b) or maize root cortex (c) samples) and promoter strength as determined in tobacco leaves (b) or maize protoplasts (c). These examples correspond to the highest correlations in (a).

Extended Data Fig. 2 Strength of maize promoters depends on the TATA box location in maize protoplasts.

a, Histogram showing the percentage of maize promoters with a TATA box at the indicated position (reproduced from Fig. 4). Three peaks in the distribution of TATA boxes are highlighted in grey. Peak 1 spans bases −72 to −65, peak 2 spans bases −59 to −50, and peak 3 spans bases −34 to −24. b, Violin plots, boxplots and significance levels (as defined in Fig. 2) of promoter strength for maize promoters without enhancer in the indicated assay system. Promoters without a TATA box (−) were compared to those with a TATA box outside (+/−) or within one of the three peaks highlighted in (a).

Extended Data Fig. 3 The BREu element is most active in maize protoplasts.

a-d, Violin plots of promoter strength in tobacco leaves (a,c) or maize protoplasts (b,d). Promoters with a strong or intermediate TATA box (motif score ≥ 0.7; see Methods) were grouped by GC content and split into promoters without (left half, darker colour) or with (right half, lighter colour) a BREu (a,b), or BREd (c,d) element. Violin plots, boxplots and significance levels are as defined in Fig. 2. Only one half is shown for violin plots. e,f, Logoplots for promoters with a BREu (e) or BREd (f) before (WT) and after (mut) introducing mutations that disrupt the elements. g, Logoplots for promoters without a BRE (WT) and with an inserted BREu (+ BREu) or BREd (+ BREd) element. h, Boxplots and significance levels (as defined in Fig. 4) for the relative strength of the promoter variants shown in (e-g). The corresponding WT promoter was set to 0 (horizontal black line).

Extended Data Fig. 4 The Y patch is a plant-specific core promoter element.

a, Histogram showing the percentage of promoters with a TATA box at the indicated position. b,c, Violin plots of promoter strength in tobacco leaves (b) or maize protoplasts (c). Promoters were grouped by GC content and split into promoters without (left half, darker colour) or with (right half, lighter colour) a Y patch. Violin plots, boxplots and significance levels are as defined in Fig. 2. Only one half is shown for violin plots.

Extended Data Fig. 5 Core promoter elements at the TSS influence promoter strength.

a-d, Violin plots of promoter strength in tobacco leaves (a,c) or maize protoplasts (b,d). Promoters were grouped by GC content and split into promoters without (left half, darker colour) or with (right half, lighter colour) an Inr (a,b), or TCT (c,d) element at the TSS. Violin plots, boxplots and significance levels are as defined in Fig. 2. Only one half is shown for violin plots.

Extended Data Fig. 6 Transcription factor binding sites contribute to promoter strength in an assay system-dependent manner.

a-d, Violin plots of promoter strength for libraries without enhancer in tobacco leaves (a,c) or maize protoplasts (b,d). Promoters were grouped by GC content and split into promoters without (left half, darker colour) or with (right half, lighter colour) a binding site for TCP (a,b) or HSF (c,d) transcription factors. Violin plots, boxplots and significance levels are as defined in Fig. 2. Only one half is shown for violin plots.

Extended Data Fig. 7 Transcription factor binding sites are more active upstream of the TATA box.

a-c, Histograms showing the number of promoters with a TCP (a), HSF (b), or NAC (c) transcription factor binding site at the indicated position. d-i, Violin plots, boxplots and significance levels (as defined in Fig. 2) of promoter strength for libraries without enhancer in tobacco leaves (d-f) or maize protoplasts (g-i). Promoters were grouped by the position of their TCP (d,g), HSF (e,h), or NAC (f,i) transcription factor binding site relative to the TATA box: either upstream (up) or downstream (down).

Extended Data Fig. 8 Promoter-proximal transcription factor binding sites influence enhancer responsiveness.

a-f, Violin plots of enhancer responsiveness in tobacco leaves (a,c,e) or maize protoplasts (b,d,f). Promoters were grouped by GC content and split into promoters without (left half, darker colour) or with (right half, lighter colour) a TCP (a,b), WRKY (c,d), or B3 (e,f) transcription factor binding site. Violin plots, boxplots and significance levels are as defined in Fig. 2. Only one half is shown for violin plots.

Extended Data Fig. 9 Mutations in transcription factor binding sites alter light-dependency.

a-c, One or two T > G mutations were introduced in binding sites for TCP (a,b) or WRKY (c) transcription factors. The orientation of a binding site in the wild type promoter determined the bases that were mutated. d, Boxplots and significance levels (as defined in Fig. 4) for the relative light-dependency of promoters harbouring mutations in the indicated transcription factor binding site as shown in (a-c). The corresponding wild type promoter was set to 0 (horizontal black line).

Extended Data Fig. 10 The in silico evolution of promoters is most effective in early rounds.

a,b, 150 native and 160 synthetic promoters were subjected to 10 rounds of in silico evolution and the strength of the evolved promoters was predicted with the tobacco model (a) or the maize model (b). The black line represents the median promoter strength after each round. c,d, Correlation (Pearson’s R2 and Spearman’s ρ) between the predicted and experimentally determined strength of promoters after 0, 3, or 10 rounds of in silico evolution. Promoter strengths measured in tobacco leaves were compared to predictions from the tobacco model (c) and the data from maize protoplasts was compared to the predictions from the maize model (d). The models used for the in silico evolution are indicated on each plot.

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Jores, T., Tonnies, J., Wrightsman, T. et al. Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters. Nat. Plants 7, 842–855 (2021). https://doi.org/10.1038/s41477-021-00932-y

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