Full-length transcriptome sequencing of pepper fruit during development and construction of a transcript variation database

Abstract

Chili pepper is an important spice and a model plant for fruit development studies. Large-scale omics information on chili pepper plant development continues to be gathered for understanding development as well as capsaicin biosynthesis. In this study, a full-spectrum transcriptome data of eight chili pepper tissues at five growth stages using the Oxford Nanopore long-read sequencing approach was generated. Of the 485 351 transcripts, 35 336 were recorded as reference transcripts (genes), while 450 015 were novel including coding, lnc, and other non-coding RNAs. These novel transcripts belonged to unknown/intergenic (347703), those retained introns (26336), and had multi-exons with at least one junction match (20333). In terms of alternative splicing, retained intron had the highest proportion (14795). The number of tissue-specific expressed transcripts ranged from 22 925 (stem) to 40 289 (flower). The expression changes during fruit and placenta development are discussed in detail. Integration of gene expression and capsaicin content quantification throughout the placental development clarifies that capsaicin biosynthesis in pepper is mainly derived from valine, leucin, and isoleucine degradation as well as citrate cycle and/or pyrimidine metabolism pathways. Most importantly, a user-friendly Pepper Full-Length Transcriptome Variation Database (PFTVD 1.0) ( http://pepper-database.cn/ ) has been developed. PFTVD 1.0 provides transcriptomics and genomics information and allows users to analyse the data using various tools implemented. This work highlights the potential of long-read sequencing to discover novel genes and transcripts and their diversity in plant developmental biology.

Acknowledgements

We would like to thank Jie Chen and Yali Li from Wuhan Benagen Technology Co., Ltd for their assistance in data analysis. This work was financially supported by Natural Science Foundation of China (32130097, 32302574, 32320103011), and the Ministry of Finance and Ministry of Agriculture and Rural Affairs of China (CARS-24-A05).

Author contributions

Conceptualization: X.Z. and L.O.; methodology: Z.L. and B.Y.; software: H.S., K.D., and L.M.; validation: Z.L., S.Y., and K.D.; formal analysis: Z.L. and T.Z; investigation: Z.L., H.S., and L.M.; resources: Z.Z., W.C., and X.Z.; data curation: T.Z. and K.D.; writing—original draft preparation: Z.L., B.Y., and T.Z; writing—review and editing: Z.L., X.Z., and L.O; visualization: H.C. and W.X.; supervision: X.Z.; project administration: L.O.; funding acquisition: X.Z. and L.O. All authors have read and agreed to the published version of the manuscript.

Data availability

All the data generated in this study have been implemented in the Pepper Full-length Transcriptome Variation Database (http://pepper-database.cn/). Raw transcriptome data are submitted to NCBI SRA: PRJNA1046572 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1046572). Supplementary tables are available at https://figshare.com/account/home#/projects/187023.

Conflict of interest statement

The authors declare no conflict of interest.

Supplementary data

Supplementary data is available at Horticulture Research online.

References

[1] Hernández-Pérez T, Gómez-García MR, Valverde ME. et al. Capsicum annuum (hot pepper): an ancient Latin-American crop with outstanding bioactive compounds and nutraceutical potential. Comp Rev Food Sci Food Saf. 2020; 19:2972-93

[2] Martínez O, Arce-Rodríguez ML, Hernández-Godínez F. et al. Transcriptome analyses throughout chili pepper fruit development reveal novel insights into the domestication process. Plan Theory. 2021; 10:585

[3] Kim S, Park M, Yeom S-I. et al. Genome sequence of the hot pepper provides insights into the evolution of pungency in capsicum species. Nat Genet. 2014; 46:270-8

[4] Kim S, Park J, Yeom S-I. et al. New reference genome sequences of hot pepper reveal the massive evolution of plant disease-resistance genes by retroduplication. Genome Biol. 2017; 18:1-11

[5] Hulse-Kemp AM, Maheshwari S, Stoffel K. et al. Reference quality assembly of the 3.5-Gb genome of Capsicum annuum from a single linked-read library. Hort Res. 2018; 5:4

[6] Qin C, Yu C, Shen Y. et al. Whole-genome sequencing of cultivated and wild peppers provides insights into capsicum domestication and specialization. Proc Natl Acad Sci. 2014; 111:5135-40

[7] Góngora-Castillo E, Fajardo-Jaime R, Fernández-Cortes A. et al. The capsicum transcriptome DB: a “hot” tool for genomic research. Bioinformation. 2012; 8:43-7

[8] Liu F, Yu H, Deng Y. et al. PepperHub, an informatics hub for the chili pepper research community. Mol Plant. 2017; 10:1129-32

[9] Aza-González C, Núñez-Palenius HG, Ochoa-Alejo N. Molecular biology of capsaicinoid biosynthesis in chili pepper (capsicum spp.). Plant Cell Rep. 2011; 30:695-706

[10] Zhang Z-X, Zhao S-N, Liu G-F. et al. Discovery of putative capsaicin biosynthetic genes by RNA-seq and digital gene expression analysis of pepper. Sci Rep. 2016; 6:34121

[11] Pandhair V, Sharma S. Accumulation of capsaicin in seed, pericarp and placenta of Capsicum annuum L fruit. J Plant Biochem Biotechnol. 2008; 17:23-7

[12] Pattison RJ, Csukasi F, Zheng Y. et al. Comprehensive tissue-specific transcriptome analysis reveals distinct regulatory programs during early tomato fruit development. Plant Physiol. 2015; 168:1684-701

[13] Guan D, Halstead MM, Islas-Trejo AD. et al. Prediction of transcript isoforms in 19 chicken tissues by Oxford nanopore long-read sequencing. Front Genet. 2022; 13:997460

[14] Glinos DA, Garborcauskas G, Hoffman P. et al. Transcriptome variation in human tissues revealed by long-read sequencing. Nature. 2022; 608:353-9

[15] Stewart MD, Ritterhoff T, Klevit RE. et al. E2 enzymes: more than just middle men. Cell Res. 2016; 26:423-40

[16] Chi YH, Koo SS, Oh HT. et al. The physiological functions of universal stress proteins and their molecular mechanism to protect plants from environmental stresses. Front. Plant Sci. 2019; 10:750

[17] Alok A, Kaur H, Bhati KK. et al. Biochemical characterization and spatio-temporal expression of myo-inositol oxygenase (MIOX) from wheat (Triticum aestivum L.). Plant. Gene. 2015; 4:10-9

[18] Jones AJ, Hirst J. A spectrophotometric coupled enzyme assay to measure the activity of succinate dehydrogenase. Anal Biochem. 2013; 442:19-23

[19] Kanayama Y. Sugar metabolism and fruit development in the tomato. Hortic J. 2017; 86:417-25

[20] Li M, Feng F, Cheng L. Expression patterns of genes involved in sugar metabolism and accumulation during apple fruit development. PLoS One. 2012; 7:e33055

[21] Chen T, Zhang Z, Li B. et al. Molecular basis for optimizing sugar metabolism and transport during fruit development. aBIOTECH. 2021; 2:330-40

[22] Batista-Silva W, Nascimento VL, Medeiros DB. et al. Modifications in organic acid profiles during fruit development and ripening: correlation or causation? Front. Plant Sci. 2018; 9:1689

[23] Wang L, Zhong Y, Liu J. et al. Pigment biosynthesis and molecular genetics of fruit color in pepper. Plan Theory. 2023; 12:2156

[24] Kim E, Magen A, Ast G. Different levels of alternative splicing among eukaryotes. Nucleic Acids Res. 2007; 35:125-31

[25] Wang M, Zheng Z, Tian Z. et al. Molecular cloning and analysis of an acetyl-CoA C-acetyltransferase gene (EkAACT) from euphorbia kansui Liou. Plan Theory. 2022; 11:1539

[26] Rodrigues-Salvador A, Lana-Costa J, Omena-Garcia RP. et al. Metabolic shifts during fruit development in pungent and non-pungent peppers. Food Chem. 2022; 375:131850

[27] de Oliveira Correa JP, Silva EM, Nogueira FT. Molecular control by non-coding RNAs during fruit development: from gynoecium patterning to fruit ripening. Front Plant Sci. 2018; 9:1760

[28] Liu S, Li W, Wu Y. et al. De novo transcriptome assembly in chili pepper (Capsicum frutescens) to identify genes involved in the biosynthesis of capsaicinoids. PLoS One. 2013; 8:e48156

[29] Hadadi N, Spiljar M, Steinbach K. et al. Comparative multi-tissue profiling reveals extensive tissue-specificity in transcriptome reprogramming during thermal adaptation. elife. 2022; 11:e78556

[30] Nicolas P, Shinozaki Y, Powell A. et al. Spatiotemporal dynamics of the tomato fruit transcriptome under prolonged water stress. Plant Physiol. 2022; 190:2557-78

[31] Polkhovskaya E, Bolotina A, Merkulov P. et al. Long-read cDNA sequencing revealed novel expressed genes and dynamic transcriptome landscape of triticale (x Triticosecale Wittmack) seed at different developing stages. Agronomy. 2023; 13:292

[32] Oikonomopoulos S, Bayega A, Fahiminiya S. et al. Methodologies for transcript profiling using long-read technologies. Front Genet. 2020; 11:606

[33] Yan X, Bai D, Song H. et al. Alternative splicing during fruit development among fleshy fruits. BMC Genomics. 2021; 22:1-14

[34] Li Y, Dai C, Hu C. et al. Global identification of alternative splicing via comparative analysis of SMRT-and Illumina-based RNA-seq in strawberry. Plant J. 2017; 90:164-76

[35] Tanaka Y, Asano T, Kanemitsu Y. et al. Positional differences of intronic transposons in pAMT affect the pungency level in chili pepper through altered splicing efficiency. Plant J. 2019; 100:693-705

[36] Martínez-López LA, Ochoa-Alejo N, Martínez O. Dynamics of the chili pepper transcriptome during fruit development. BMC Genomics. 2014; 15:143-18

[37] Jang YK, Jung ES, Lee H-A. et al. Metabolomic characterization of hot pepper (Capsicum annuum “CM334”) during fruit development. J Agric Food Chem. 2015; 63:9452-60

[38] Tanaka Y, Watachi M, Nemoto W. et al. Capsaicinoid biosynthesis in the pericarp of chili pepper fruits is associated with a placental septum-like transcriptome profile and tissue structure. Plant Cell Rep. 2021; 40:1859-74

[39] Heng Z, Xu X, Xu X. et al. Integrated transcriptomic and metabolomic analysis of chili pepper fruits provides new insight into the regulation of the branched chain esters and capsaicin biosynthesis. Food Res Int. 2023; 169:112856

[40] Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage years. Nat Rev Genet. 2019; 20:631-56

[41] Tanaka Y, Nakashima F, Kirii E. et al. Difference in capsaicinoid biosynthesis gene expression in the pericarp reveals elevation of capsaicinoid contents in chili peppers (Capsicum chinense). Plant Cell Rep. 2017; 36:267-79

[42] Li H, Handsaker B, Wysoker A. et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009; 25:2078-9

[43] Pertea G, Pertea M. GFF utilities: GffRead and GffCompare. F1000Research. 2020; 9:304

[44] Deng Y, Li J, Wu S. et al. Integrated nr database in protein annotation system and its localization. Comput Eng. 2006; 32:71-4

[45] Bateman A, Birney E, Cerruti L. et al. The Pfam protein families database. Nucleic Acids Res. 2002; 30:276-80

[46] Apweiler R, Bairoch A, Wu CH. et al. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2004; 32:D115-9

[47] Kanehisa M. The KEGG database. In: Bock G,Goode JA (eds), Novartis Foundation Symposium. Wiley Online Library, 2002, 91-100

[48] Ashburner M, Ball CA, Blake JA. et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000; 25:25-9

[49] Tatusov RL, Galperin MY, Natale DA. et al. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000; 28:33-6

[50] Kanehisa M, Goto S, Kawashima S. et al. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004; 32:D277-80

[51] Trincado JL, Entizne JC, Hysenaj G. et al.SUPPA2:fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions. Genome Biol. 2018; 19:1-11

[52] Sun L, Luo H, Bu D. et al. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Res. 2013; 41:e166-6

[53] Kong L, Zhang Y, Ye Z-Q. et al. CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res. 2007; 35:W345-9

[54] Untergasser A, Cutcutache I, Koressaar T. et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 2012; 40:e115-5

[55] Taylor SC, Nadeau K, Abbasi M. et al. The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends Biotechnol. 2019; 37:761-74

[56] Mestdagh P, Van Vlierberghe P, De Weer A. et al. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009; 10:R64-10