Skip to main content

Comprehensive transcriptome resource for response to phytohormone-induced signaling in Capsicum annuum L.

Abstract

Objectives

Phytohormones are small signaling molecules with crucial roles in plant growth, development, and environmental adaptation to biotic and abiotic stress responses. Despite several previously published molecular studies focused on plant hormones, our understanding of the transcriptome induced by phytohormones remains unclear, especially in major crops. Here, we aimed to provide transcriptome dataset using RNA sequencing for phytohormone-induced signaling in plant.

Data description

We used high-throughput RNA sequencing profiling to investigate the pepper plant response to treatment with four major phytohormones (salicylic acid, jasmonic acid, ethylene, and abscisic acid). This dataset yielded 78 samples containing three biological replicates per six different time points for each treatment and the control, constituting 187.8 Gb of transcriptome data (2.4 Gb of each sample). This comprehensive parallel transcriptome data provides valuable information for understanding the relationships and molecular networks that regulate the expression of phytohormone-related genes involved in plant developments and environmental stress adaptation.

Objective

Plants are sessile beings, which are exposed to various attacks from the environment involving biotic/abiotic stress conditions [1, 2]. Besides, plants interact with positive effects from plant-associated microbes which induce phytohormones so that strengthen plants to withstand stresses. In response to these physiological processes, different signaling pathways of plant hormones are activated. Infection of plants with diverse pathogens results in changes in the level of various phytohormones. Three phytohormones—salicylic acid (SA), jasmonic acid (JA) and ethylene (ET), are known to regulating plant defense responses against various pathogens, pests and abiotic stresses. Abscisic acid (ABA) exert opposite defense effect from these hormones, but can also enhance disease resistance [3, 4]. These phytohormones tend to act interdependently through complex antagonistic or synergistic interactions [5]. These relationships reveal that important networks of phytohormone crosstalk exist to mediate physiological processes such as biotic, abiotic stress tolerance, and plant growth.

Despite several previously reported molecular studies focused on plant hormones, the transcriptome information of phytohormones remains unclear, especially in major crops [6, 7]. Recently a few genes and gene families regulated by phytohormones have been identified in pepper [8,9,10], but a time-series investigation of the well-regulated transcriptome network has yet to be performed. Accordingly, this study aimed to provide transcriptome dataset using RNA sequencing (RNA-seq) for transcriptome dataset of phytohormone-induced signaling in pepper plant. In this study, we performed transcriptome analysis of pepper treated with four major phytohormones, namely SA, JA, ET, and ABA, at six time points. Total 78 RNA samples were subjected to RNA-seq by constructing strand-specific RNA libraries, and 187.8 Gb of transcriptome data were produced. These transcriptomic profiles will contribute to our understanding of the phytohormone-induced signaling pathways involved in response to environmental stresses and plant development in pepper and other crops.

Data description

Plant materials and treatment

Pepper seeds (C. annuum cv. Bukang) were sown on petri dish lined with a wet tissue layer for 2 weeks. After germination, seedlings were transplanted into a 32-cell plug seedling tray and grown at 24 ± 1 °C with an alternating 16-h light/8-h dark photoperiod. At the 6-true-leaf stage, pepper plants were sprayed with 5 mM sodium salicylate (SA), 100 μM methyl jasmonate (JA), 5 mM ethephone (ET), 100 μM ( ±)-ABA, or distilled water (mock) [11,12,13,14]. Each was treated and incubated in the growth chamber separately to avoid cross-contamination. After treatment, the third or fourth leaf was collected at 0, 1, 3, 6, 12, and 24 h post-inoculation, and frozen with liquid nitrogen immediately prior to storage at − 80 °C. Each treatment time point was performed for three biological replicates, and leaves from four healthy plants were gathered for a replicate.

RNA extraction, library construction, and sequencing

Following phytohormone inoculation, total RNA from pepper leaves was extracted using Trizol reagent (Ambion, USA) according to the manufacturer’s instructions. To confirm the phytohormone response for each treatment, semi-quantitative RT-PCR was performed using gene primers such as SA (CaPR1), JA (CaPin2), ET (CaACO), and ABA (CaWRKY40) [13,14,15,16]. Expression levels were normalized with the CaActin [17] and the mock group was used as a control (Data file 1).

Samples of total RNA (5 μg) were used to prepare strand-specific libraries as described previously [18, 19]. In brief, from each total RNA, the Poly-(A) RNA was captured and fragmented by the size of 300 to 400 bp. The RNA fragments were generated second-strand cDNA, and then performed end-repair, dA tailing, adapter ligation and PCR amplification. We generated total 78 cDNA libraries consisting of four treatment groups and a mock control group, for transcriptome profiling. Strand-specific RNA libraries were sequenced using the 151nt paired-end on the HiSeq2500 platform (Illumina, USA) at Macrogen Corporation (Korea) (Table 1).

Table 1 Overview of data files/data sets

Quality control and quantification of gene expression

The adapter filtering and quality trimming was performed on a total of 78 RNA libraries using the Cutadapt and Trimmomatic programs, respectively [20,21,22]. The read length of each sample was filtered by QC and the read length was 28.87–6.07 Gb (Data file 2). After filtering, the quality of pre-processed reads were checked using FastQC [23] and the output was merged using MultiQC (Data file 3) [24]. Read mapping was carried out with the C. annuum ‘CM334’ reference genome v.1.6 (https://peppergenome.snu.ac.kr) using Hisat2 [25]. Transcriptome quantification was performed using HTseq-count [26] to calculate the read counts. The clean reads were mapped to the coding DNA sequence with 65.75–70.36% and the genome with 92.13 –96.04% (Data file 2). Raw read count was normalized to FPKM and visualized with the distribution (Data files 3, 4). The principal component analysis (PCA) with normalized data was used to examine sample variation (Data file 3) [27, 28]. The comparisons between PC1 and PC2 (SA, ET) or PC1 and PC3 (JA, ABA) indicated that the mock and phytohormone-treated groups were separated clearly.

Limitations

Raw data was deposited in NCBI, and quality filtering is required before use. The transcriptome data was generated using C. annuum cv. Bukang, and read mapping was carried out with C. annuum cv. CM334 reference genome.

Availability of supporting data

The RNA-seq library of 78 samples are publicly available from the Sequence Read Archive at accession number https://identifiers.org/ncbi/insdc.sra:SRP265260 [29]. The quantified transcriptome expression data was deposited in the NCBI Gene Expression Omnibus database with an accession number of https://identifiers.org/geo:GSE149037 [30]. The combined additional files and information generated in this study have been uploaded to figshare, with accession number https://doi.org/10.6084/m9.figshare.12319337.v6 [31].

Abbreviations

ABA:

Abscisic acid

ET:

Ethylene

FPKM:

Fragments per kilobase of transcripts per million mapped reads

PCA:

Principal component analysis

QC:

Quality control

RNA-seq:

RNA sequencing

RT-PCR:

Reverse transcription polymerase chain reaction

SA:

Salicylic acid

References

  1. 1.

    Lee HA, Yeom SI. Plant NB-LRR proteins: tightly regulated sensors in a complex manner. Brief. Funct. Genomics. 2015;14(4):233–42.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Kang WH, Yeom SI. Genome-wide identification, classification, and expression analysis of the receptor-like protein family in tomato. Plant Pathol. J. 2018;34(5):435–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Lee SC, Luan S. ABA signal transduction at the crossroad of biotic and abiotic stress responses. Plant Cell Environ. 2012;35(1):53–60.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Sewelam N, Kazan K, Schenk PM. Global plant stress signaling: Reactive oxygen species at the cross-road. Front. Plant Sci. 2016;7:187.

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Shigenaga AM, Argueso CT. No hormone to rule them all: Interactions of plant hormones during the responses of plants to pathogens. Semin. Cell Dev. Biol. 2016;56:174–89.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Berens ML, Berry HM, Mine A, Argueso CT, Tsuda K. Evolution of hormone signaling networks in plant defense. Annu. Rev. Phytopathol. 2017;55:401–25.

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Shigenaga AM, Berens ML, Tsuda K, Argueso CT. Towards engineering of hormonal crosstalk in plant immunity. Curr. Opin. Plant Biol. 2017;38:164–72.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Lim CW, Lee SC. Functional roles of the pepper MLO protein gene, CaMLO2, in abscisic acid signaling and drought sensitivity. Plant Mol. Biol. 2014;85(1–2):1–10.

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Jing H, Li C, Ma F, Ma JH, Khan A, Wang X, et al. Genome-wide identification, expression diversication of dehydrin gene family and characterization of CaDHN3 in pepper (Capsicum annuum L.). PLoS ONE. 2016;11(8):e0161073.

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Sarde SJ, Bouwmeester K, Venegas Molina J, David A, Boland W, Dicke M. Involvement of sweet pepper CaLOX2 in jasmonate-dependent induced defence against Western flower thrips. J. Integr. Plant Biol. 2019;61(10):1085–98.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Kim KJ, Park CJ, An JM, Ham BK, Lee BJ, Paek KH. CaAlaAT1 catalyzes the alanine: 2-oxoglutarate aminotransferase reaction during the resistance response against Tobacco mosaic virus in hot pepper. Planta. 2005;221(6):857–67.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Do HM, Lee SC, Jung HW, Sohn KH, Hwang BK. Differential expression and in situ localization of a pepper defensin (CADEF1) gene in response to pathogen infection, abiotic elicitors and environmental stresses in Capsicum annuum. Plant Sci. 2004;166(5):1297–305.

    CAS  Article  Google Scholar 

  13. 13.

    Lee SJ, Lee MY, Yi SY, Oh SK, Choi SH, Her NH, et al. PPI1: a novel pathogen-induced basic region-leucine zipper (bZIP) transcription factor from pepper. Mol. Plant Microbe Interact. 2002;15(6):540–8.

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Kim YC, Kim SY, Paek KH, Choi D, Park JM. Suppression of CaCYP1, a novel cytochrome P450 gene, compromises the basal pathogen defense response of pepper plants. Biochem. Biophys. Res. Commun. 2006;345(2):638–45.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Dang FF, Wang YN, Yu L, Eulgem T, Lai Y, Liu ZQ, et al. CaWRKY40, a WRKY protein of pepper, plays an important role in the regulation of tolerance to heat stress and resistance to Ralstonia solanacearum infection. Plant Cell Environ. 2013;36(4):757–74.

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Peña-Cortés H, Fisahn J, Willmitzer L. Signals involved in wound-induced proteinase inhibitor II gene expression in tomato and potato plants. Proc. Natl. Acad. Sci. USA. 1995;92(10):4106–13.

    PubMed  Article  Google Scholar 

  17. 17.

    Yeom SI, Seo E, Oh SK, Kim KW, Choi D. A common plant cell-wall protein HyPRP1 has dual roles as a positive regulator of cell death and a negative regulator of basal defense against pathogens. Plant J. 2012;69(5):755–68.

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Zhong S, Joung JG, Zheng Y, Chen YR, Liu B, Shao Y, et al. High-throughput illumina strand-specific RNA sequencing library preparation. Cold Spring Harb. Protoc. 2011;2011(8):940–9.

    PubMed  Article  Google Scholar 

  19. 19.

    Kang WH, Sim YM, Koo N, Nam JY, Lee J, Kim N, et al. Transcriptome profiling of abiotic responses to heat, cold, salt, and osmotic stress of Capsicum annuum L. Sci. Data. 2020;7(1):1–7.

    Article  Google Scholar 

  20. 20.

    Ranzani V, Arrigoni A, Rossetti G, Panzeri I, Abrignani S, Bonnal RJ, et al. Next-generation sequencing analysis of long noncoding RNAs in CD4+ T cell differentiation. Methods Mol. Biol. 2017;1514:173–85.

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10–2.

    Article  Google Scholar 

  22. 22.

    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–200.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Andrews S. FastQC: A Quality Control Tool for High Throughput Sequence Data. Cambridge: Babraham Bioinformatics; 2010.

    Google Scholar 

  24. 24.

    Ewels P, Magnusson M, Lundin S, Kaller M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Kim D, Langmead B, Salzberg SL. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods. 2015;12(4):357–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Anders S, Pyl PT, Huber W. HTSeq—A Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9.

    CAS  Article  Google Scholar 

  27. 27.

    Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom. Intell. Lab. Syst. 1987;2(1–3):37–52.

    CAS  Article  Google Scholar 

  28. 28.

    Kim MS, Kim S, Jeon J, Kim KT, Lee HA, Lee HY, et al. Global gene expression profiling for fruit organs and pathogen infections in the pepper Capsicum annuum L. Sci. Data. 2018;5:180103.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Lee J, Nam JY, Jang H, Kim N, Kim YM, Kang WH, et al. Comprehensive transcriptome profiling for response to phytohormone-induced signaling in Capsicum annuum L. NCBI Sequence Read Archive. 2020. https://identifiers.org/ncbi/insdc.sra:SRP265260.

  30. 30.

    Lee J, Nam JY, Jang H, Kim N, Kim YM, Kang WH, et al. Comprehensive transcriptome profiling for response to phytohormone-induced signaling in Capsicum annuum L. Gene Expression Omnibus. 2020. https://identifiers.org/geo:GSE149037.

  31. 31.

    Lee J, Nam JY, Jang H, Kim N, Kim YM, Kang WH, et al. Comprehensive transcriptome resource for response to phytohormone-induced signaling in Capsicum annuum L. figshare. 2020. https://doi.org/10.6084/m9.figshare.12319337.v6.

Download references

Acknowledgements

We appreciate the support from the KRIBB initiative program.

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean Government (NRF-2017R1E1A1A01072843, 2015R1A6A1A03031413 and 2019R1C1C1007472). H.J., N.K., and J.L. were supported by a scholarship from the BK21 Program from the Ministry of Education.

Author information

Affiliations

Authors

Contributions

J-YN collected samples, analyzed data, and wrote the manuscript. JL performed data analysis and wrote the manuscript draft. NK, Y-MK, and HJ collected samples and generated transcriptome data. W-HK and S-IY designed the experiments, organized and wrote the manuscript, and supervised the project. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Won-Hee Kang or Seon-In Yeom.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lee, J., Nam, J., Jang, H. et al. Comprehensive transcriptome resource for response to phytohormone-induced signaling in Capsicum annuum L.. BMC Res Notes 13, 440 (2020). https://doi.org/10.1186/s13104-020-05281-1

Download citation

Keywords

  • Transcriptome
  • Phytohormone signaling
  • Environmental stresses
  • Capsicum annuum