Skip to main content

Genomics and Metabolomics: A Strategy for Elucidation of Metabolic Pathways in Medicinal Plants

  • Chapter
  • First Online:
Phytochemical Genomics

Abstract

Medicinal plants are storehouse of numerous phytochemicals which are structurally and functionally divergent group of molecules. The biosynthesis of these molecules is channelized through complex network of metabolic pathways, which are evolved in response to various biotic and abiotic stress factors. As the concentration of these metabolites in the field grown plants is abysmally low and do not meet the demand, metabolic pathway analysis, and subsequent engineering for augmented in vitro production of these phytochemicals, is an alternative proposition to sustain the growth of phyto-pharmaceutical industries. Broadly, integration of metabolite profiling with genomic techniques including transcriptome analysis could lead to identify the rate limiting step(s) in the pathway and subsequently the gene(s) of interest. However, there is no single step technology available for the detection of all phytochemicals synthesized by a plant and hence combination of analytical techniques, both qualitative and quantitative, such as GC-MS, LC-MS, CE-MS, FT-IR, NMR, etc., are required. Similarly, as the complexity of phytochemicals increases, there is proportionate increase in the biochemical steps such as, derivatization/cyclization and modification of the molecules, making the whole pathway elucidation more cumbersome and difficult. Interestingly, the application of elicitation techniques along with specific metabolite inhibitors including enzyme inhibitor studies coupled with gene expression analysis is added techniques, in pathway analysis. In plant cell culture studies, elicitors have shown to trigger significant changes in metabolite production and corresponding gene expression, leading to annotation of genes(s) involved in the metabolite biosynthesis. Therefore, an integrated analysis of metabolic pathway including metabolic flux, gene expression, enzyme activity, elicitors, and transcriptome studies will be strategic for sustained in vitro production of phytochemicals, in future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Antonio, AdS., Oliveira, D.S., Cardoso dos Santos, G.R., Pereira, H.M.G., Wiedemann, L.S.M., da Veiga-Junior, V.F, 2021. UHPLC-HRMS/MS on untargeted metabolomics: a case study with Copaifera (Fabaceae). RSC Adv 11, 25096–25103

    Article  CAS  Google Scholar 

  • Antunes AC, Acunha TS, Perin EC, Rombaldi CV, Galli V, Chaves FC (2019) Untargeted metabolomics of strawberry (Fragaria x ananassa ‘Camarosa’) fruit from plants grown under osmotic stress conditions. J Sci Food Agric 99:6973–6980

    Article  CAS  Google Scholar 

  • Arbona V, Manzi M, Ollas CD, Gómez-Cadenas A (2013) Metabolomics as a tool to investigate abiotic stress tolerance in plants. Int J Mol Sci 14(3):4885–4911

    Article  CAS  Google Scholar 

  • Bains S, Thakur V, Kaur J, Singh K, Kaur R (2019) Elucidating genes involved in sesquiterpenoid and flavonoid biosynthetic pathways in Saussurea lappa by de novo leaf transcriptome analysis. Genomics 111:1474–1482. https://doi.org/10.1016/j.ygeno.2018.09.022

    Article  CAS  Google Scholar 

  • Barrales-Cureño HJ, Montiel-Montoya J, Espinoza-Pérez J, Cortez-Ruiz JA, Lucho-Constantino GG, Zaragoza-Martínez F, Salazar-Magallón JA, Reyes C, Lorenzo-Laureano J, López-Valdez LG (2021) Metabolomics and fluxomics studies in the medicinal plant Catharanthus roseus. In: Medicinal and aromatic plants. Elsevier, Amsterdam, pp 61–86

    Chapter  Google Scholar 

  • Burgess K, Rankin N, Weidt S (2014) Metabolomics. In: Handbook of pharmacogenomics and stratified medicine. Elsevier, Amsterdam, pp 181–205

    Chapter  Google Scholar 

  • Carrera FP, Noceda C, Maridueña-Zavala MG, Cevallos-Cevallos JM (2021) Metabolomics, a powerful tool for understanding plant abiotic stress. Agronomy 11:824

    Article  Google Scholar 

  • Cascante M, Marin S (2008) Metabolomics and fluxomics approaches. Essays Biochem 45:67–82

    Article  CAS  Google Scholar 

  • Cavill R, Jennen D, Kleinjans J, Briedé JJ (2016) Transcriptomic and metabolomic data integration. Brief Bioinform 17:891–901. https://doi.org/10.1093/bib/bbv090

    Article  Google Scholar 

  • Chen J (2004) A novel gene identification approach: massively parallel signature sequencing. Prog Biochem Biophys 31:761–765

    Google Scholar 

  • Chen Y, Liu YS, Zeng JG (2014) Progresses on plant genome sequencing profile. Life Sci Res 18:66–74

    Google Scholar 

  • Cocuron J-C, Koubaa M, Kimmelfield R, Ross Z, Alonso AP (2019) A combined metabolomics and fluxomics analysis identifies steps limiting oil synthesis in maize embryos. Plant Physiol 181:961–975

    Article  CAS  Google Scholar 

  • Di Masi S, De Benedetto GE, Malitesta C, Saponari M, Citti C, Cannazza G, Ciccarella G (2022) HPLC-MS/MS method applied to an untargeted metabolomics approach for the diagnosis of “olive quick decline syndrome”. Anal Bioanal Chem 414:465–473

    Article  Google Scholar 

  • Ding J, Ruan C, Guan Y, Li H, Du W, Lu S, Wen X, Tang K, Chen Y (2022) Nontargeted metabolomic and multigene expression analyses reveal the mechanism of oil biosynthesis in sea buckthorn berry pulp rich in palmitoleic acid. Food Chem 374:131719

    Article  CAS  Google Scholar 

  • Eichten SR, Vaughn MW, Hermanson PJ, Springer NM (2013) Variation in DNA methylation patterns is more common among maize inbreds than among tissues. Plant Genome 6. https://doi.org/10.3835/plantgenome2012.06.0009

  • Feng CH, Hei CY, Wang Y, Zeng YF, Zhang JG (2019) Phylogenetic position of Chosenia arbutufolia in the Salicaceae inferred from whole chloroplast genome. For Res 32:73–77. https://doi.org/10.13275/j.cnki.lykxyj.2019.02.011

    Article  Google Scholar 

  • Feng X, Yu Q, Li B, Kan J (2022) Comparative analysis of carotenoids and metabolite characteristics in discolored red pepper and normal red pepper based on non-targeted metabolomics. LWT 153:112398

    Article  CAS  Google Scholar 

  • Galbiatti MI, Pinheiro GP, Antunes ERM, Hernandes VV, Sawaya ACHF (2021) Effect of environmental factors on Plectranthus neochilus volatile composition: a GC-MS-based metabolomics approach. Planta Med Int Open 8:e153–e160

    Article  Google Scholar 

  • Grabherr MG, Haas BJ, Yassour M, Levin JZ, Amit I (2013) Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat Biotechnol 29:644–652. https://doi.org/10.1038/nbt.1883

    Article  CAS  Google Scholar 

  • Guo J, Huang Z, Sun J, Cui X, Liu Y (2021) Research progress and future development trends in medicinal plant transcriptomics. Front Plant Sci 12:1520.s

    Article  Google Scholar 

  • Guttman M, Garber M, Levin JZ, Donaghey J, Robinson J, Adiconis X et al (2010) Corrigendum: ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol 28:503–510. https://doi.org/10.1038/nbt0710-756b

    Article  CAS  Google Scholar 

  • Hall RD (2011) Plant metabolomics in a nutshell: potential and future challenges. In: Hall RD (ed) Annual plant reviews, vol 43. Wiley-Blackwell, Oxford, UK, pp 1–24

    Google Scholar 

  • Hazrati H, Fomsgaard IS, Kudsk P (2021) Targeted metabolomics unveil alteration in accumulation and root exudation of flavonoids as a response to interspecific competition. J Plant Interact 16:53–63

    Article  Google Scholar 

  • Jayakodi M, Lee SC, Park HS, Jang WJ, Lee YS, Choi BS et al (2014) Transcriptome profiling and comparative analysis of Panax ginseng adventitious roots. J Ginseng Res 38:278–288. https://doi.org/10.1016/j.jgr.2014.05.008

    Article  Google Scholar 

  • Jia CL, Zhang Y, Zhu L, Zhang R (2015) Application progress of transcriptome sequencing technology in biological sequencing. Mol Plant Breed 13:2388–2394

    CAS  Google Scholar 

  • Lee S, Oh D-G, Singh D, Lee HJ, Kim GR, Lee S, Lee JS, Lee CH (2019) Untargeted metabolomics toward systematic characterization of antioxidant compounds in Betulaceae family plant extracts. Metabolites 9:186

    Article  CAS  Google Scholar 

  • Li YM, Li SX, Li XS, Li CY (2018) Transcriptome studies with the third-generation sequencing technology. Life Sci Instrum 16:114–121

    Google Scholar 

  • Li WW, Sun Y, Yuan Y, Yu JL, Chen QQ, Ge YL et al (2020) Isolation and genomic analyses of SARS-CoV-2 in Anhui Province, China. Bing Du Xue Bao 36:751–757. https://doi.org/10.13242/j.cnki.bingduxuebao.003795

    Article  Google Scholar 

  • Liao WF, Mei ZN, Miao LH, Liu PL, Gao RJ (2020) Comparative transcriptome analysis of root, stem, and leaf tissues of Entada phaseoloides reveals potential genes involved in triterpenoid saponin biosynthesis. BMC Genomics 21:639. https://doi.org/10.1186/s12864-020-07056-1

    Article  CAS  Google Scholar 

  • Liu FX, Yang WG, Sun QH (2018a) Transcriptome sequencing data analysis and high-throughput GO annotation. J Anhui Agric Univ 46:88–91. https://doi.org/10.13989/j.cnki.0517-6611.2018.31.027+100

    Article  Google Scholar 

  • Liu MM, Zhu JH, Wu SB, Wang CK, Guo XY, Wu JW et al (2018b) De novo assembly and analysis of the Artemisia argyi transcriptome and identification of genes involved in terpenoid biosynthesis. Sci Rep 8:1236–1243. https://doi.org/10.1038/s41598-018-24201-9

    Article  CAS  Google Scholar 

  • Lu X (2013) A comparison of transcriptome assembly software for next generation sequencing technologies. PhD thesis, University of LanZhou, Gansu

    Google Scholar 

  • Ma D-M, Gandra SVS, Manoharlal R, La Hovary C, Xie D-Y (2019a) Untargeted metabolomics of Nicotiana tabacum grown in United States and India characterizes the association of plant metabolomes with natural climate and geography. Front Plant Sci 10:1370

    Article  Google Scholar 

  • Ma LN, Yang JB, Ding YF, Li YK (2019b) Research progress on three generations sequencing technology and its application. China Anim Husb Vet Med 46:2246–2256. https://doi.org/10.16431/j.cnki.1671-7236.2019.08.007

    Article  Google Scholar 

  • Mareya CR, Tugizimana F, Piater LA, Madala NE, Steenkamp PA, Dubery IA (2019) Untargeted metabolomics reveal defensome-related metabolic reprogramming in Sorghum bicolor against infection by Burkholderia andropogonis. Metabolites 9:8

    Article  Google Scholar 

  • Martin J, Bruno VM, Fang Z, Meng X, Blow M, Tao Z et al (2010) Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads. BMC Genomics 11:663. https://doi.org/10.1186/1471-2164-11-663

    Article  CAS  Google Scholar 

  • Mecha E, Erny GL, Guerreiro ACL, Feliciano RP, Barbosa I, Bento da Silva A, Leitão ST, Veloso MM, Rubiales D, Rodriguez-Mateos A, Figueira ME, Vaz Patto MC, Bronze MR (2022) Metabolomics profile responses to changing environments in a common bean (Phaseolus vulgaris L.) germplasm collection. Food Chem 370:131003

    Article  CAS  Google Scholar 

  • Mei C, Wang H, Zan L, Cheng G, Li A, Zhao C, Wang H (2016) Research progress on animal genome research based on high-throughput sequencing technology. J Northwest A & F Univ Nat Sci Ed 44(3):43–51

    Google Scholar 

  • Mironova VV, Weinholdt C, Grosse I (2015) RNA-seq data analysis for studying abiotic stress in horticultural plants. In: Abiotic stress biology in horticultural plants. Springer, Tokyo, pp 197–220

    Google Scholar 

  • Morrison JA, Woldemariam M (2022) Metabolomic responses of indigenous and nonindigenous plants to deer exclosure fencing and deer herbivory in a suburban forest (preprint). Preprints

    Google Scholar 

  • Mun HI, Kwon MC, Lee N-R, Son SY, Song DH, Lee CH (2021) Comparing metabolites and functional properties of various tomatoes using mass spectrometry-based metabolomics approach. Front Nutr 8:659646

    Article  Google Scholar 

  • Niedringhaus TP, Milanova D, Kerby MB, Snyder MP, Barron AE (2011) Landscape of next-generation sequencing technologies. Anal Chem 83:4327–4341. https://doi.org/10.1021/ac2010857

    Article  CAS  Google Scholar 

  • Oliver S (1998) Systematic functional analysis of the yeast genome. Trends Biotechnol 16:373–378

    Article  CAS  Google Scholar 

  • Pereira Braga C, Adamec J (2019) Metabolome analysis. In: Encyclopedia of bioinformatics and computational biology. Elsevier, Amsterdam, pp 463–475

    Chapter  Google Scholar 

  • Sanchez-Arcos C, Kai M, Svatoš A, Gershenzon J, Kunert G (2019) Untargeted metabolomics approach reveals differences in host plant chemistry before and after infestation with different pea aphid host races. Front Plant Sci 10:188

    Article  Google Scholar 

  • Sárosi S, Sipos L, Kókai Z, Pluhár Z, Szilvássy B, Novák I (2013) Effect of different drying techniques on the aroma profile of Thymus vulgaris analyzed by GC–MS and sensory profile methods. Ind Crop Prod 46:210–216

    Article  Google Scholar 

  • Schulz MH, Zerbino DR, Vingron M, Birney E (2012) Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 28:1086–1092. https://doi.org/10.1093/bioinformatics/bts094

    Article  CAS  Google Scholar 

  • Shah M, Alharby HF, Hakeem KR, Ali N, Rahman IU, Munawar M et al (2020) De novo transcriptome analysis of Lantana camara L. revealed candidate genes involved in phenylpropanoid biosynthesis pathway. Sci Rep 10:467–486. https://doi.org/10.1038/s41598-020-70635-5

    Article  CAS  Google Scholar 

  • Simkin AJ, Guirimand G, Papon N, Courdavault V, Thabet I, Ginis O, Bouzid S, Giglioli-Guivarc’h N, Clastre M (2011) Peroxisomal localisation of the final steps of the mevalonic acid pathway in planta. Planta 234:903–914. https://doi.org/10.1007/s00425-011-1444-6

    Article  CAS  Google Scholar 

  • Slisz AM, Breksa AP, Mishchuk DO, McCollum G, Slupsky CM (2012) Metabolomic analysis of citrus infection by ‘Candidatus Liberibacter’ reveals insight into pathogenicity. J Proteome Res 11:4223–4230

    Article  CAS  Google Scholar 

  • Strickler SR, Bombarely A, Mueller LA (2012) Designing a transcriptome next-generation sequencing project for a nonmodel plant species. Am J Bot 99:257–266. https://doi.org/10.3732/ajb.1100292

    Article  CAS  Google Scholar 

  • Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31:46–53. https://doi.org/10.1038/nbt.2450

    Article  CAS  Google Scholar 

  • Vrhovsek U, Masuero D, Gasperotti M, Franceschi P, Caputi L, Viola R, Mattivi F (2012) A versatile targeted metabolomics method for the rapid quantification of multiple classes of phenolics in fruits and beverages. J Agric Food Chem 60:8831–8840

    Article  CAS  Google Scholar 

  • Wahman R, Cruzeiro C, Graßmann J, Schröder P, Letzel T (2022) The changes in Lemna minor metabolomic profile: a response to diclofenac incubation. Chemosphere 287:132078

    Article  CAS  Google Scholar 

  • Wang X, Tang C, Zhang G, Li Y, Wang C, Liu B, Qu Z, Zhao J, Han Q, Huang L, Chen X, Kang Z (2009) cDNA-AFLP analysis reveals differential gene expression in compatible interaction of wheat challenged with Puccinia striiformis f. sp. tritici. BMC Genomics 10:289. https://doi.org/10.1186/1471-2164-10-289

    Article  CAS  Google Scholar 

  • Wang C, Zhu J, Liu M, Yang QS, Wu JW, Li ZG (2018) De novo sequencing and transcriptome assembly of Arisaema heterophyllum Blume and identification of genes involved in isoflavonoid biosynthesis. Sci Rep 8:17643. https://doi.org/10.1038/s41598-018-35664-1

    Article  CAS  Google Scholar 

  • Wu YQ, Guo J, Zhou Q, Xin Y, Wang GB, Xu LA (2018) De novo transcriptome analysis revealed genes involved in flavonoid biosynthesis, transport and regulation in Ginkgo biloba. Ind Crop Prod 124:226–235. https://doi.org/10.1016/j.indcrop.2018.07.060

    Article  CAS  Google Scholar 

  • Xia J, Guo Z, Fang S, Gu J, Liang X (2021) Effect of drying methods on volatile compounds of burdock (Arctium lappa L.) root tea as revealed by gas chromatography mass spectrometry-based metabolomics. Foods 10:868

    Article  CAS  Google Scholar 

  • Xiao M, Zhang Y, Chen X, Lee E-J, Barber CJS, Chakrabarty R, Desgagné-Penix I, Haslam TM, Kim Y-B, Liu E, MacNevin G, Masada-Atsumi S, Reed DW, Stout JM, Zerbe P, Zhang Y, Bohlmann J, Covello PS, De Luca V, Page JE, Ro D-K, Martin VJJ, Facchini PJ, Sensen CW (2013) Transcriptome analysis based on next-generation sequencing of non-model plants producing specialized metabolites of biotechnological interest. J Biotechnol 166:122–134. https://doi.org/10.1016/j.jbiotec.2013.04.004

    Article  CAS  Google Scholar 

  • Xie Y, Wu G, Tang J, Luo R, Jordan P, Liu S et al (2014) SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics 12:1660–1666. https://doi.org/10.1093/bioinformatics/btu077

    Article  CAS  Google Scholar 

  • Xie X, Tang T, Wang W, Tang X, Zhang J, Wang Z (2021) Metabolomics clarify the compounds contributing to the quality of apples among different regions in China. J Food Process Preserv 45:e15054

    Article  CAS  Google Scholar 

  • Yan JL, Qian LH, Zhu WD, Qiu JR, Lu QJ, Wang XB et al (2020) Integrated analysis of the transcriptome and metabolome of purple and green leaves of Tetrastigma hemsleyanum reveals gene expression patterns involved in anthocyanin biosynthesis. PLoS One 15:e0230154. https://doi.org/10.1371/journal.pone.0230154

    Article  CAS  Google Scholar 

  • Yan H, Pu Z-J, Zhang Z-Y, Zhou G-S, Zou D-Q, Guo S, Li C, Zhan Z-L, Duan J-A (2021) Research on biomarkers of different growth periods and different drying processes of Citrus wilsonii Tanaka based on plant metabolomics. Front Plant Sci 12:700367

    Article  Google Scholar 

  • Ye S, Wang Z, Shen J, Shao Q, Fang H, Zheng B, Younis A (2019) Sensory qualities, aroma components, and bioactive compounds of Anoectochilus roxburghii (Wall.) Lindl. as affected by different drying methods. Ind Crop Prod 134:80–88

    Article  CAS  Google Scholar 

  • Ye Y, Zhang X, Chen X, Xu Y, Liu J, Tan J, Li W, Tembrock LR, Wu Z, Zhu G (2022) The use of widely targeted metabolomics profiling to quantify differences in medicinally important compounds from five Curcuma (Zingiberaceae) species. Ind Crop Prod 175:114289

    Article  CAS  Google Scholar 

  • Yuan X, Li K, Huo W, Lu X (2018) De novo transcriptome sequencing and analysis to identify genes involved in the biosynthesis of flavonoids in Abrus mollis leave. Russ J Plant Physiol 65:333–344

    Article  CAS  Google Scholar 

  • Zhang Q, Sheng J (2008) Development and application of gene chip technology. Zhongguo Yi Xue Ke Xue Yuan Xue Bao 30:344–347

    CAS  Google Scholar 

  • Zhang DY, Zhang TX, Wang GX (2016) Development and application of second- generation sequencing technology. Environ Sci Technol 39:96–102. https://doi.org/10.3969/j.issn.1003-6504.2016.09.017

    Article  Google Scholar 

  • Zhang S, Li C, Gu W, Qiu R, Chao J, Pei L, Ma L, Guo Y, Tian R (2021) Metabolomics analysis of dandelions from different geographical regions in China. Phytochem Anal 32:899–906

    Article  CAS  Google Scholar 

  • Zheng J, Johnson M, Mandal R, Wishart DS (2021) A comprehensive targeted metabolomics assay for crop plant sample analysis. Metabolites 11:303

    Article  CAS  Google Scholar 

  • Zhong S, Fei Z, Chen YR, Zheng Y, Huang M, Vrebalov J, McQuinn R, Gapper N, Liu B, Xiang J, Shao Y, Giovannoni JJ (2013) Single-base resolution methylomes of tomato fruit development reveal epigenome modifications associated with ripening. Nat Biotechnol 31(2):154–159

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Padmesh P. Pillai .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Surendran, K., Ranjisha, K.R., Aswati Nair, R., Pillai, P.P. (2022). Genomics and Metabolomics: A Strategy for Elucidation of Metabolic Pathways in Medicinal Plants. In: Swamy, M.K., Kumar, A. (eds) Phytochemical Genomics. Springer, Singapore. https://doi.org/10.1007/978-981-19-5779-6_13

Download citation

Publish with us

Policies and ethics