Protoplast transient expression-based RNA-sequencing: A simple method to screen transcriptional regulation in plants

Abstract

Protoplast transient expression-based RNA-sequencing identifies Opaque2 targets supported by molecular evidence and expression quantitative trait loci.

Dear Editor,

Transcription factors (TFs) are major modulators of gene regulation in plant cells. However, identifying targets of TFs is challenging. Conventional RNA-sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) are widely used, and each has advantages and drawbacks. The common bottleneck is that these techniques require mutants or transgenic plants, thereby making the identification of TF-regulated networks low-throughput and labor-consuming. Here, we develop a protoplast transient expression-based RNA-sequencing (PER-seq) technology to screen TF targets. The PER-seq technique employs the transient expression of GFP-labeled TFs with subsequent RNA-seq (Fig. 1A). Compared with conventional transcriptome analysis, PER-seq takes advantage of homogeneous cell type (protoplast)-based sequencing, allowing a rapid detection of transcriptional regulations with higher throughput.

Figure 1.

Identification of O2 targets using the PER-seq technique. A) Workflow of the PER-seq technique. B) Scatterplot of −log₁₀(FDR) versus log₂(fold change) of all the genes tested for differential expression. DEGs are represented by red (upregulated) and blue (downregulated) dots (FDR < 0.05). C) Heat map exhibiting expression profiles of high-confidence O2-targets identified by PER-seq and previous RNA-seq data based on the o2 mutants and WT (Li et al. 2015; Zhan et al. 2018). The color scale indicates log₁₀ (FPKM + 1). D) The O2 target screening efficiency of PER-seq and previous studies. E) The distribution of reported O2-binding motifs in DEGs from PER-seq data. F) Sequence logo of top O2-binding motifs identified by MEME program using 0.5-, 1-, and 2-kb promoters of upregulated genes from the PER-seq data. G) EMSA results confirm that O2 directly binds to promoters of identified targets. H) O2 activates transcriptions from the promoter of identified targets in the transient dual-luciferase assay. I) RT-qPCR data show decreased expression of identified targets in the o2 mutant. The expression of ZmActin1 was used as an internal control. Data are presented as means with Sd (n = 3 biological replicates) in H) and I). For all statistical analyses: ***P < 0.001, ****P < 0.0001; Student's t-test. J) Distant eQTLs regulating the expression of ZmABH3 and ZmZIM21 in developing kernels. K) The O2-regulated network coordinates seed development. The eQTLs are supported by data from this and the previous study (Li et al. 2021).

Opaque2 (O2) is a basic leucine zipper family TF crucial for maize (Zea mays) grain filling (Deng et al. 2020; Dai et al. 2021). O2 targets have been identified using ChIP-seq combined with transcriptome analysis of the o2 mutant and wild-type (WT) (Li et al. 2015; Zhan et al. 2018). Moreover, recent studies reported that O2 participated in seed size regulation through the O2-ZmGRAS11 module (Li et al. 2021; Ji et al. 2022). However, ZmGRAS11 was not identified as an O2 target through previous genome-wide screening, indicating that the regulatory function of O2 remains to be fully explored.

Given the crucial role of O2 in seed development and to better understand the O2-regulated network, we screened its targets using the PER-seq technique (Supplemental Materials and Methods). A previous transcriptome analysis using the o2 mutant and WT detected 1,024 downregulated and 839 upregulated genes in the o2 mutant using a false discovery rate (FDR) <0.05 as the cutoff threshold (Zhan et al. 2018), while another study found 767 downregulated and 838 upregulated genes in the o2 mutant (P < 0.05) (Li et al. 2015). Based on the same criterion (FDR < 0.05), we identified 1,715 O2-activated and 772 O2-repressed genes in the PER-seq data (Fig. 1B and Supplemental Table S1), exhibiting more differentially expressed genes (DEGs) compared with transcriptome analysis using the o2 mutant and WT (Li et al. 2015; Zhan et al. 2018). To evaluate the screening efficiency, we summarized high-confidence O2-regulated genes supported by at least one validation experiment according to the published data (Supplemental Table S2). Many canonical O2 targets (66%) show significant upregulation in the PER-seq data, and the screening efficiency is comparable with previous genome-wide studies (43% to 74%) using ChIP-seq and conventional RNA-seq (Fig. 1, C and D). Next, we found that almost all DEGs have at least one previously reported O2-binding site (Fig. 1E and Supplemental Table S3). These results raise the possibility that PER-seq has the potential to predict conserved TF-binding sites. Thus, we analyzed the conserved motifs in the 0.5-, 1-, and 2-kb promoter of O2-activated genes using the MEME program (https://meme-suite.org/meme/), and all these analyses revealed the core sequence of well-known O2-binding motifs GCN4 and GCN4-like (Fig. 1F).

Moreover, we identified several O2 targets (Fig. 1C and Supplemental Table S2), which were not simultaneously characterized in previous transcriptome analyses based on the o2 mutant and WT (Li et al. 2015; Zhan et al. 2018). To verify the O2 targets, we determined that O2 can directly bind to and transactivate the promoters using an electrophoretic mobility shift assay (EMSA) and a dual-luciferase transactivation assay, respectively (Fig. 1, G and H). Additionally, we tested whether O2 regulates these genes in the native biological context (kernel). Reverse transcription quantitative PCR (RT-qPCR) data show that the expression levels of all candidate genes are decreased in the kernels from the o2 mutant at 20 d after pollination (DAP) (Fig. 1I and Supplemental Fig. S1). Moreover, we analyzed whether these O2 targets have expression quantitative trait loci (eQTLs) regulating their expression in the 15 DAP kernels (Fig. 1J and Supplemental Fig. S2). All genes show trans-eQTL signals in the region of bin7.01 containing O2 (−log₁₀P > 5). Furthermore, abscisic acid 80-hydroxylase3 (ZmABH3) and zinc-finger protein expressed in inflorescence meristem 21 (ZmZIM21) have exclusive trans-eQTL signals at this region, suggesting that O2 is the major regulator for their expression in seeds. The canonical O2 targets, 15-kD β-zein (ZmZp15) and cytoplasmic pyruvate orthophosphate dikinase1 (ZmcyPPDK1), were applied as positive controls (Supplemental Fig. S3). Together, these results demonstrated that sugars will eventually be exported transporter 4c (ZmSWEET4c), ZmABH3, multidrug resistance associated protein6 (ZmMRP6), ZmMYB106, and ZmZIM21 are targets of O2. Thus, these transcriptional interactions indicate potential functions of O2.

Sugar import into seeds is essential for storage reserve synthesis and seed size. ZmSWEET4c encodes a hexose transporter that mediates the transport of hexoses from the maternal phloem into seeds (Sosso et al. 2015). Our data show that ZmSWEET4c is a direct target of O2, suggesting that O2 regulates carbohydrate import and is essential for sink strength. Zinc serves as a component of TFs and thereby is crucial for seed development. We identified that O2 directly transactivates ZmMRP6, a candidate gene responsible for Zn accumulation in maize seedlings (Ma et al. 2021), indicating that O2 plays a role in mediating mineral micronutrient transport.

Recent studies revealed that the O2-ZmGRAS11 module modulates synergistic endosperm enlargement with grain filling (Li et al. 2021; Ji et al. 2022). Here, we show that ZmZIM21, encoding a JAZ family protein, is directly regulated by O2. Intriguingly, the expression of ZmZIM21 is preferentially expressed in the endosperm (Supplemental Fig. S4) and controlled by a strong trans-eQTL containing O2 (Fig. 1J). Moreover, JAZ proteins positively regulate seed size in rice (Oryza sativa) and soybean (Glycine max) (Hu et al. 2021, 2023), suggesting that an O2-JAZ module potentially mediates synergistic endosperm expansion with storage reserve accumulation.

Previous studies revealed that O2 regulates abiotic stress-responsive genes (Zhan et al. 2018). ZmABH3, preferentially expressed in endosperm, is a candidate gene associated with cold stress tolerance (Zhang et al. 2021), while MYB family TFs play diverse roles in development, metabolism, and stress responses (Dubos et al. 2010). Our study identifies ZmABH3 and ZmMYB106 as O2 targets, supporting the potential roles of O2 in stress responses. Together, our PER-seq data expand the functional understanding of the O2-regulated network (Fig. 1K).

To test whether PER-seq can be applied to other plants, we screened the downstream genes regulated by BASIC HELIX–LOOP–HELIX34 (AtbHLH34), a regulator of iron homeostasis, in mesophyll protoplasts isolated from Arabidopsis (Arabidopsis thaliana) (Li et al. 2016). We obtained 2,611 AtbHLH34-activated and 371 AtbHLH34-repressed genes (fold change > 2 and FDR < 0.05). Compared with RNA-seq analyses using WT and the mutants (Li et al. 2016), 93% (13/14) of reported transcriptional regulations can be identified using the PER-seq method (Supplemental Fig. S5 and Tables S4–S7). This result suggests that PER-seq is a versatile method to screen transcriptional regulations in both monocots and dicots.

In summary, we demonstrate that PER-seq is a simple, labor-saving, and high-throughput method for identifying the TF-regulated network. However, PER-seq has some noteworthy limitations. If PER-seq is used in nonnative cells where the TF is typically not expressed, it may generate dysregulated genes and miss some regulations. The main reason is that correct transcriptional regulations depend on a proper set of coactivators and accessible chromatin context (affected by histone modifications and DNA methylation), which usually differ by cell and tissue type. Ectopic expression may lead to the improper function of a TF. Thus, it is essential to validate the putative regulations in the native biological context where the TF functions to obtain the true extent of a transcriptional regulatory network. Nevertheless, PER-seq can be an alternative approach when mutant or transgenic plants are unavailable or these materials have developmental phenotypes with feedback or lethal effects that affect transcriptome analysis. The homogeneous cell type sequencing nature also provides PER-seq with relatively high screening efficiency. These features make PER-seq a versatile method complementary to conventional techniques to screen transcriptional regulations.

Author contributions

X.Z. conceived and designed the experiments. J.Z. and S.L. performed experiments, analyzed the data, and wrote the manuscript. D.L., S.M., and X.L. participated in the nucleic acid–protein interaction analyses. B.W. involved in bioinformatics analyses. H.J. and W.Y. contributed to gene expression analyses. X.Z. and R.C. supervised the project and revised the manuscript.

Supplemental data

The following materials are available in the online version of this article.

Supplemental Figure S1 . RT-qPCR data show decreased expression of identified O2 targets in the o2 mutant normalized to the expression of ZmUbiquitin.

Supplemental Figure S2 . Distant eQTLs regulating the expression of ZmSWEET4c, ZmMYB106, and ZmMRP6 in developing kernels.

Supplemental Figure S3. Verification of transactivation of 15-kD β-zein (ZmZp15) and cytoplasmic pyruvate orthophosphate dikinase1 (ZmcyPPDK1) by O2.

Supplemental Figure S4. Expression of O2 and ZmZIM21 in various maize organs.

Supplemental Figure S5. Identification of AtbHLH34 targets using the PER-seq technique.

Supplemental Materials and Methods . The methodology used in this study.

Supplemental Table S1 . DEGs between O2-GFP and GFP-expressed cells.

Supplemental Table S2 . High-confidence O2-regulated genes supported by at least one validation experiment according to the published data.

Supplemental Table S3 . Reported O2-binding sites.

Supplemental Table S4 . DEGs between AtbHLH34-GFP and GFP-expressed cells.

Supplemental Table S5 . Known genes transcriptional regulated by AtbHLH34 in Arabidopsis.

Supplemental Table S6 . The quality control and mapping statistics of the RNA sequencing data.

Supplemental Table S7 . Primers and probes used in this study.

Funding

This study was supported by grants from the National Key Research and Development Program of China to X.Z. (2021YFF1000300), the National Natural Science Foundation of China to R.C. (32272118), and the National Special Program for GMO Development of China to R.C. (2016ZX08003-002).

Data availability

The data supporting the findings of this study have been provided in the text and the supplementary data files. All RNA-seq data are available through the NCBI accession PRJNA951804 and PRJNA993736.

Dive Curated Terms

The following phenotypic, genotypic, and functional terms are of significance to the work described in this paper:

• ZmABH3 Gramene: Zm00001d050021

• ZmABH3 Araport: Zm00001d050021

• ZmZIM21 Gramene: Zm00001d033048

• ZmZIM21 Araport: Zm00001d033048

• ZmGRAS11 Gramene: Zm00001d009646

• ZmGRAS11 Araport: Zm00001d009646

• ZmSWEET4c Gramene: Zm00001d015912

• ZmSWEET4c Araport: Zm00001d015912

• ZmMRP6 Gramene: Zm00001d009243

• ZmMRP6 Araport: Zm00001d009243

• ZmMYB106 Gramene: Zm00001d037334

• ZmMYB106 Araport: Zm00001d037334

• Opaque2 Gramene: Zm00001d018971

• Opaque2 Araport: Zm00001d018971