Skip to main navigation menu Skip to main content Skip to site footer

Research Articles

Early Access

Unlocking medicinal potential: Comparative transcriptome profiling of Aegle marmelos fruit and leaf tissues reveals key functional pathways

DOI
https://doi.org/10.14719/pst.9004
Submitted
21 April 2025
Published
03-11-2025
Versions

Abstract

The study aims to provide a comprehensive comparative transcriptome analysis of Aegle marmelos (L.) Correa, focusing on its fruit and leaf tissues, with the goal of expanding genomic knowledge and identifying tissue-specific gene expression related to its therapeutic properties. High-throughput RNA sequencing (RNA-seq) was employed to generate transcriptomic data from both fruit (47 million clean reads) and leaf (34 million clean reads) tissues of A. marmelos. The resulting sequences were assembled into 61860 unigenes, with 83 % and 89 % of the fruit and leaf-derived transcripts, respectively, mapping to the de novo assembly. Gene Ontology (GO) annotations were performed to categorize the molecular functions of the identified genes. The analysis revealed distinct tissue-specific expression profiles, with 14578 genes exclusive to the leaf and 11086 exclusive to the fruit. The predominant molecular functions were associated with binding, catalytic activity and transporter processes. The data demonstrated significant differences in gene expression between the two tissues, highlighting their divergent roles in the plant’s metabolic and therapeutic functions. This study provides the first in-depth genomic resources for A. marmelos, enhancing the understanding of its biological and pharmacological potential. The identification of tissue-specific genes offers insights into the molecular mechanisms behind its therapeutic properties, supporting further investigation into its biosynthetic pathways. RNA sequencing emerges as a critical tool for exploring neglected horticultural species, paving the way for targeted research and the discovery of novel bioactive compounds.

References

  1. 1. Mujeeb F, Bajpai P, Pathak N, Verma SR. Genetic diversity analysis of medicinally important horticultural crop Aegle marmelos by ISSR markers. PCR Methods Protoc. 2017;195-211. https://doi.org/10.1007/978-1-4939-7060-5_14
  2. 2. Warrier R, Viji J, Priyadharshini P. In vitro propagation of Aegle marmelos L. (Corr.) from mature trees through enhanced axillary branching. Asian J Exp Biol Sci. 2010;1(3):669-76.
  3. 3. Sharma CK, Sharma V. Analysis of Aegle marmelos (L.) Corr. diversity using citrus-based microsatellite markers. J Appl Hortic. 2015;17(3):217-21. https://doi.org/10.37855/jah.2015.v17i03.41
  4. 4. Patil S, Muthusamy P. A bio-inspired approach of formulation and evaluation of Aegle marmelos fruit extract mediated silver nanoparticle gel and comparison of its antibacterial activity with antiseptic cream. Eur J Integr Med. 2020;33:101025. https://doi.org/10.1016/j.eujim.2019.101025
  5. 5. Varier A. A dictionary of Indian raw materials and industrial products. New Delhi: Publications and Information Directorate, Council of Scientific and Industrial Research; 2002:387.
  6. 6. Nayak D, Singh DR, Sabarinathan P, Singh S, Nayak T. Random amplified polymorphic DNA (RAPD) markers reveal genetic diversity in bael (Aegle marmelos Correa) genotypes of Andaman Islands, India. Afr J Biotechnol. 2013;12(42). https://doi.org/10.5897/AJB2013.12473
  7. 7. De Britto AJ, Mahesh R, Sujin RM. Molecular characterization of Aegle marmelos (L.) Correa ex Roxb. using RAPD markers. J Swamy Bot Cl. 2009;26:27-32.
  8. 8. Kaushik P, Kumar S. Data of de novo assembly of the leaf transcriptome in Aegle marmelos. Data Brief. 2018;19:700-3. https://doi.org/10.1016/j.dib.2018.05.095
  9. 9. Chang E, Shi S, Liu J, Cheng T, Xue L, Yang X, et al. Selection of reference genes for quantitative gene expression studies in Platycladus orientalis (Cupressaceae) using real-time PCR. PLoS One. 2012;7(3):e33278. https://doi.org/10.1371/journal.pone.0033278
  10. 10. Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 2011;21:1543-51. https://doi.org/10.1101/gr.121095.111
  11. 11. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat Biotechnol. 2011;29(7):644-52. https://doi.org/10.1038/nbt.1883
  12. 12. Davidson NM, Oshlack A. Corset: enabling differential gene expression analysis for de novo assembled transcriptomes. Genome Biol. 2014;15:1-4. https://doi.org/10.1186/s13059-014-0410-6
  13. 13. Liu C, Dou Y, Guan X, Fu Q, Zhang Z, Hu Z, et al. De novo transcriptomic analysis and development of EST-SSRs for Sorbus pohuashanensis (Hance) Hedl. PLoS One. 2017;12(6):e0179219. https://doi.org/10.1371/journal.pone.0179219
  14. 14. Srivastava KK, Singh HK. Physico-chemical quality of bael (Aegle marmelos Correa) cultivars. Agric Sci Dig. 2004;24(1):65-6.
  15. 15. Tripathi PC, Sane A, Kumar P, Chaturvedi K, Mishra DS, Ravat P. Phenotypic diversity and genetic characterization of Cordia myxa L. using multivariate analysis. Flora. 2025;323:152673. https://doi.org/10.1016/j.flora.2025.152673
  16. 16. Tunç Y, Aydınlıoglu C, Yılmaz KU, Khadivi A, Mishra DS, Sakar EH, et al. Determination of genetic diversity in persimmon accessions using morphological and inter-simple sequence repeat markers. Sci Rep. 2025;15(1):2297. https://doi.org/10.1038/s41598-025-86101-z
  17. 17. Cock PJ, Fields CJ, Goto N, Heuer ML, Rice PM. The Sanger FASTQ file format for sequences with quality scores and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 2010;38(6):1767-71. https://doi.org/10.1093/nar/gkp1137
  18. 18. Feng C, Chen M, Xu CJ, Bai L, Yin XR, Li X, et al. Transcriptomic analysis of Chinese bayberry (Myrica rubra) fruit development and ripening using RNA-seq. BMC Genomics. 2012;13:1-15. https://doi.org/10.1186/1471-2164-13-19
  19. 19. Sivalingam D, Rajendran R, Anbarasan K. DNA barcoding of highly threatened sacred plant Aegle marmelos L. Int J Curr Res Biosci Plant Biol. 2016;3:88-96. https://doi.org/10.20546/ijcrbp.2016.305.014
  20. 20. Weitzel C, Simonsen HT. Cytochrome P450-enzymes involved in the biosynthesis of mono- and sesquiterpenes. Phytochem Rev. 2015;14:7-24. https://doi.org/10.1007/s11101-013-9280-x
  21. 21. Rasool S, Mohamed R. Plant cytochrome P450s: nomenclature and involvement in natural product biosynthesis. Protoplasma. 2016;253:1197-209. https://doi.org/10.1007/s00709-015-0884-4
  22. 22. Qi X, Yu X, Xu D, Fang H, Dong K, Li W, et al. Identification and analysis of CYP450 genes from transcriptome of Lonicera japonica and expression analysis of chlorogenic acid biosynthesis-related CYP450s. PeerJ. 2017;5:e3781. https://doi.org/10.7717/peerj.3781
  23. 23. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59-60. https://doi.org/10.1038/nmeth.3176
  24. 24. Latchman DS. Transcription factors: an overview. Int J Biochem Cell Biol. 1997;29(12):1305-12. https://doi.org/10.1016/S1357-2725(97)00085-X
  25. 25. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 2013;14(6):671-83. https://doi.org/10.1093/bib/bbs046
  26. 26. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:1-2. https://doi.org/10.1186/gb-2010-11-2-r14
  27. 27. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR, et al. The Pfam protein families database. Nucleic Acids Res. 2007;36:D281-8. https://doi.org/10.1093/nar/gkm960
  28. 28. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28(5):511-15. https://doi.org/10.1038/nbt.1621
  29. 29. Zheng B, Zhao Q, Wu H, Ma X, Xu W, Li L, et al. Metabolomics and transcriptomics analyses reveal the potential molecular mechanisms of flavonoids and carotenoids in guava pulp with different colors. Sci Hortic. 2022;305:111384. https://doi.org/10.1016/j.scienta.2022.111384
  30. 30. Perveen N, Dinesh MR, Sankaran M, Ravishankar KV, Krishnajee HG, Hanur VS, et al. Comparative transcriptome analysis provides novel insights into molecular response of salt-tolerant and sensitive polyembryonic mango genotypes to salinity stress at seedling stage. Front Plant Sci. 2023;14:1152485. https://doi.org/10.3389/fpls.2023.1152485
  31. 31. Lu X, Cao X, Li F, Li J, Xiong J, Long G, et al. Comparative transcriptome analysis reveals a global insight into molecular processes regulating citrate accumulation in sweet orange (Citrus sinensis). Physiol Plant. 2016;158(4):463-82. https://doi.org/10.1111/ppl.12484

Downloads

Download data is not yet available.