Identification and characterization of 13 gene families encoding enzymes involved in flavonoid biosynthesis in barley and their roles under abiotic stress

identification-and-characterization-of-13-gene-families-encoding-enzymes-involved-in-flavonoid-biosynthesis-in-barley-and-their-roles-under-abiotic-stress
Identification and characterization of 13 gene families encoding enzymes involved in flavonoid biosynthesis in barley and their roles under abiotic stress

References

  1. Shen, N. et al. Plant flavonoids: Classification, distribution, biosynthesis, and antioxidant activity. Food Chem. 383, 132531 (2022).

    Google Scholar 

  2. Li, P., Ruan, Z., Fei, Z., Yan, J. & Tang, G. Integrated transcriptome and metabolome analysis revealed that flavonoid biosynthesis may dominate the resistance of Zanthoxylum bungeanum against stem canker. J. Agric. Food Chem. 69, 6360–6378 (2021).

    Google Scholar 

  3. Deshmukh, A. B. et al. De novo root transcriptome of a medicinally important rare tree Oroxylum indicum for characterization of the flavonoid biosynthesis pathway. Phytochemistry 156, 201–213 (2018).

    Google Scholar 

  4. Ni, J. et al. Ethylene mediates the branching of the jasmonate-induced flavonoid biosynthesis pathway by suppressing anthocyanin biosynthesis in red Chinese pear fruits. Plant Biotechnol. J. 18, 1223–1240 (2020).

    Google Scholar 

  5. Liu, W. et al. The flavonoid biosynthesis network in plants. Int. J. Mol. Sci. 22, 1–18 (2021).

    Google Scholar 

  6. Wang, J., Zhang, C. & Li, Y. Genome-wide identification and expression profiles of 13 key structural gene families involved in the biosynthesis of rice flavonoid scaffolds. Genes https://doi.org/10.3390/genes13030410 (2022).

    Google Scholar 

  7. Liu, S. et al. Identification and characterization of thirteen gene families involved in flavonoid biosynthesis in Ginkgo biloba. Ind. Crops Prod. 188, 115576 (2022).

    Google Scholar 

  8. Kubra, G. et al. Expression characterization of flavonoid biosynthetic pathway genes and transcription factors in peanut under water deficit conditions. Front. Plant Sci. 12, 680368 (2021).

    Google Scholar 

  9. Yu, C. et al. Genomic and transcriptomic studies on flavonoid biosynthesis in Lagerstroemia indica. BMC Plant Biol. 24, 171 (2024).

    Google Scholar 

  10. Wang, Y. et al. Metabolomic and transcriptomic analysis of flavonoid biosynthesis in two main cultivars of Actinidia arguta Sieb.Zucc. grown in Northern China. Front. Plant Sci. 13, 1–15 (2022).

    Google Scholar 

  11. Yin, Q. et al. Genome-wide identification and functional characterization of UDP-glucosyltransferase genes involved in flavonoid biosynthesis in glycine max. Plant Cell Physiol. 58, 1558–1572 (2017).

    Google Scholar 

  12. Kuo, Y.-T., Chao, Y.-T., Chen, W.-C., Shih, M.-C. & Chang, S.-B. Segmental and tandem chromosome duplications led to divergent evolution of the chalcone synthase gene family in Phalaenopsis orchids. Ann. Bot. 123, 69–77 (2019).

    Google Scholar 

  13. Wu, X. et al. Chalcone synthase (CHS) family members analysis from eggplant (Solanum melongena L.) in the flavonoid biosynthetic pathway and expression patterns in response to heat stress. PLoS One 15, e0226537 (2020).

    Google Scholar 

  14. Emelianova, K., Martínez Martínez, A., Campos-Dominguez, L. & Kidner, C. Multi-tissue transcriptome analysis of two Begonia species reveals dynamic patterns of evolution in the chalcone synthase gene family. Sci. Rep. 11, 17773 (2021).

    Google Scholar 

  15. Chao, N. et al. Functional characterization of two chalcone isomerase (CHI) revealing their responsibility for anthocyanins accumulation in mulberry. Plant Physiol. Biochem. 161, 65–73 (2021).

    Google Scholar 

  16. Schulz, E., Tohge, T., Zuther, E., Fernie, A. R. & Hincha, D. K. Natural variation in flavonol and anthocyanin metabolism during cold acclimation in A rabidopsis thaliana accessions. Plant Cell Environ. 38, 1658–1672 (2015).

    Google Scholar 

  17. Li, P. et al. The Arabidopsis UDP-glycosyltransferases UGT79B2 and UGT79B3, contribute to cold, salt and drought stress tolerance via modulating anthocyanin accumulation. Plant J. 89, 85–103 (2017).

    Google Scholar 

  18. Hasan, M. & Rima, R. Genetic engineering to improve essential and conditionally essential amino acids in maize : transporter engineering as a reference genetic engineering to improve essential and conditionally essential amino acids in maize : transporter engineering as a ref. Transgenic Res. 30, 207–220 (2022).

    Google Scholar 

  19. Zhu, L., Ding, Y., Wang, S., Wang, Z. & Dai, L. Genome-wide identification, characterization, and expression analysis of CHS gene family members in Chrysanthemum nankingense. Genes https://doi.org/10.3390/genes13112145 (2022).

    Google Scholar 

  20. Cheng, Y., Zhang, M., Cao, X., Mao, J. & Chen, B. Identification and expression analysis of CHS gene family in grape. Journal of fruit science 40 (2023).

  21. Zhang, Y. et al. Identification of flavanone 3-hydroxylase gene family in strawberry and expression analysis of fruit at different coloring stages. Int. J. Mol. Sci. https://doi.org/10.3390/ijms242316807 (2023).

    Google Scholar 

  22. Han, Y. et al. Functional analysis of two flavanone-3-hydroxylase genes from camellia sinensis: a critical role in flavonoid accumulation. Genes 8, 300 (2017).

    Google Scholar 

  23. Falcone Ferreyra, M. L. et al. The identification of maize and Arabidopsis type I flavone synthases links flavones with hormones and biotic interactions. Plant Physiol. 169, 1090–1107 (2015).

    Google Scholar 

  24. Liu, S., Ju, J. & Xia, G. Identification of the flavonoid 3′-hydroxylase and flavonoid 3′,5′-hydroxylase genes from Antarctic moss and their regulation during abiotic stress. Gene 543, 145–152 (2014).

    Google Scholar 

  25. Yue, L. et al. Genome-wide identification and characterization of flavonol synthase (FLS) gene family in Brassica vegetables and their roles in response to biotic and abiotic stresses. Sci. Hortic. 331, 113168 (2024).

    Google Scholar 

  26. Qian, X. et al. Identification and expression analysis of DFR gene family in brassica napus L. Plants 12, 2583 (2023).

    Google Scholar 

  27. Han, Y., Ding, T., Su, B. & Jiang, H. Genome-wide identification, characterization and expression analysis of the chalcone synthase family in maize. Int. J. Mol. Sci. https://doi.org/10.3390/ijms17020161 (2016).

    Google Scholar 

  28. Isa, A., Garba, A. A., Sabo, M. U. & Fagam, A. S. Evaluation of morphological traits of barley (hordeum vulgare L.) varieties in different inter-row spacings and nitrogen rates under irrigation. Niger. J. Agric. Agric. Technol. 5, 166–175 (2025).

    Google Scholar 

  29. Ancuța, B. E., Muntean, L. & Russu, F. Barley (Hordeum vulgare L.): Medicinal and therapeutic uses – review. Hop Med. Plants 27, 87–95 (2019).

    Google Scholar 

  30. Blake, T., Blake, V. C., Bowman, J. G. P. & Abdel-Haleem, H. Barley feed uses and quality improvement. Barley: production, improvement, and uses 522, (2011).

  31. Munns, R. & Tester, M. Mechanisms of salinity tolerance. Annu. Rev. Plant Biol. 59, 651–681 (2008).

    Google Scholar 

  32. Wiegmann, M. et al. Barley yield formation under abiotic stress depends on the interplay between flowering time genes and environmental cues. Sci. Rep. 9, 6397 (2019).

    Google Scholar 

  33. Korn, M., Peterek, S., Mock, H., Heyer, A. G. & Hincha, D. K. Heterosis in the freezing tolerance, and sugar and flavonoid contents of crosses between Arabidopsis thaliana accessions of widely varying freezing tolerance. Plant Cell Environ. 31, 813–827 (2008).

    Google Scholar 

  34. Pennisi, E. The blue revolution, drop by drop, gene by gene. Science 320, 171–173 (2008).

    Google Scholar 

  35. Kowalczewski, P. Ł et al. Influence of abiotic stress factors on the antioxidant properties and polyphenols profile composition of green barley (Hordeum vulgare L.). Int. J. Mol. Sci. 21, 397 (2020).

    Google Scholar 

  36. Chen, C. et al. Tbtools: An integrative toolkit developed for interactive analyses of big biological data. Mol. Plant 13, 1194–1202 (2020).

    Google Scholar 

  37. Rabby, M. et al. Comprehensive analysis of the oligopeptide transporter gene family in maize: Genome-wide identification, structural characterization, and stress- responsive expression. S. Afr. J. Bot. https://doi.org/10.1016/j.sajb.2024.10.004 (2024).

    Google Scholar 

  38. Mia, S. et al. In silico evolutionary origin , structural properties , molecular docking , following expression analysis of the nitrate transporters in maize to explore their roles in abiotic stress tolerance. Physiol. Mol. Biol. Plants https://doi.org/10.1007/s12298-025-01669-0 (2025).

    Google Scholar 

  39. Tamura, K., Stecher, G. & Kumar, S. MEGA11 : Molecular evolutionary genetics analysis version 11. Molecular biology and evolution, 1–6 (2021).

  40. Lin, Z. & Chen, F. Comprehensive genome-wide identification and expression profiling of eceriferum ( CER ) gene family in passion fruit ( Passiflora edulis ) under fusarium kyushuense and drought stress conditions. Front. Plant Sci. 13, 584 (2022).

    Google Scholar 

  41. Song, S. et al. Genome-wide identification and expression analyses of the aquaporin gene family in Passion fruit (Passiflora edulis), revealing PeTIP3-2 to be involved in drought stress. Int. J. Mol. Sci. 23, 1–23 (2022).

    Google Scholar 

  42. Islam, M. N., Rabby, M. G., Hossen, M. M., Bonny, M. & Hasan, M. M. Genome-wide identification following functional analysis of amino acid permease and cationic amino acid transporter gene families in maize and their role in drought stress. S. Afr. J. Bot. 168, 360–371 (2024).

    Google Scholar 

  43. Mia, S. et al. Molecular characterization following expression analysis of sugar transporters in passion fruit to explore their roles in fruit development and abiotic stress tolerance. 3 Biotech 1–28, (2026).

  44. Huang, D. et al. Genome-wide association and expression analysis of the lipoxygenase gene family in Passiflora edulis revealing PeLOX4 might be involved in fruit ripeness and ester formation. Int. J. Mol. Sci. https://doi.org/10.3390/ijms232012496 (2022).

    Google Scholar 

  45. Kesawat, M. S. et al. Genome-wide identification and characterization of the brassinazole-resistant (BZR) gene family and its expression in the various developmental stage and stress conditions in wheat (triticum aestivum L). Int. J. Mol. Sci. 22, 8743 (2021).

    Google Scholar 

  46. Kumar, A. et al. Genome-wide identification and in silico analysis of NPF, NRT2, CLC and SLAC1/SLAH nitrate transporters in hexaploid wheat (Triticum aestivum). Sci. Rep. 12, 1–20 (2022).

    Google Scholar 

  47. Jeyasri, R. et al. The role of OsWRKY genes in rice when faced with single and multiple abiotic stresses. Agronomy https://doi.org/10.3390/agronomy11071301 (2021).

    Google Scholar 

  48. Ge, S. X., Jung, D., Jung, D. & Yao, R. ShinyGO: A graphical gene-set enrichment tool for animals and plants. Bioinformatics 36, 2628–2629 (2020).

    Google Scholar 

  49. Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53, D672–D677 (2025).

    Google Scholar 

  50. Hasan, M., Mia, S. & Yang, J. Molecular mechanisms of how black barley accumulates higher anthocyanins than blue barley following transcriptomic evaluation and expression analysis of key genes in anthocyanins biosynthesis pathway. Front. Plant Sci. https://doi.org/10.3389/fpls.2025.1650803 (2025).

    Google Scholar 

  51. Islam, N., Rabby, G., Hossen, M. & Kamal, M. In silico functional and pathway analysis of risk genes and SNPs for type 2 diabetes in Asian population. PLoS One https://doi.org/10.1371/journal.pone.0268826 (2022).

    Google Scholar 

  52. Shadhin, M. S. T. et al. In silico functional, structural, and pathogenicity assessment of single nucleotide polymorphisms in the human SOX9 gene. Sci. Rep. https://doi.org/10.1038/s41598-025-30462-y (2025).

    Google Scholar 

  53. Ismail, N. A. & Jusoh, S. A. Molecular docking and molecular dynamics simulation studies to predict flavonoid binding on the surface of DENV2 E protein. Interdiscip. Sci. Comput. Life Sci. 9, 499–511 (2017).

    Google Scholar 

  54. Grasso, G. et al. Fragmented blind docking : a novel protein – ligand binding prediction protocol. J. Biomol. Structure and. Dyn. 40, 13472–13481 (2022).

    Google Scholar 

  55. Morris, G. M. et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791 (2009).

    Google Scholar 

  56. Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2. 0: new docking methods, expanded force field, and python bindings. J. Chem. Inf. Model. 61, 3891–3898 (2021).

    Google Scholar 

  57. Shaweta, S., Akhil, S. & Utsav, G. Molecular docking studies on the anti-fungal activity of Allium sativum (Garlic) against Mucormycosis (black fungus) by BIOVIA discovery studio visualizer 21.1.0.0. Ann. Antivirals Antiretrovir. 5, 028–032 (2021).

    Google Scholar 

  58. Ahammad, F. et al. Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein. Brief. Bioinform. 22, bbab098 (2021).

    Google Scholar 

  59. Mia, M. S. et al. Genome-wide exploration: Evolution, structural characterization, molecular docking, molecular dynamics simulation and expression analysis of sugar transporter (ST) gene family in potato (Solanum tuberosum). Comput. Biol. Chem. 117, 108402 (2025).

    Google Scholar 

  60. Mark, P. & Nilsson, L. Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J. Phys. Chem. A 105, 9954–9960 (2001).

    Google Scholar 

  61. Roos, K. et al. OPLS3e: extending force field coverage for drug-like small molecules. J. Chem. Theor. Comput. 15, 1863–1874 (2019).

    Google Scholar 

  62. Kamal, M. M. et al. In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene. Sci. Rep. 14, 1–18 (2024).

    Google Scholar 

  63. Song, Z. et al. Identification and characterization of yellow stripe-like genes in maize suggest their roles in the uptake and transport of zinc and iron. BMC Plant Biol. 24, 1–17 (2024).

    Google Scholar 

  64. Mazhar, H. S. U. D. et al. Genome-wide identification, and in-silico expression analysis of YABBY gene family in response to biotic and abiotic stresses in potato (Solanum tuberosum). Genes https://doi.org/10.3390/genes14040824 (2023).

    Google Scholar 

  65. He, A. et al. Genome-wide identification and expression analysis of the SPL gene family and its response to abiotic stress in barley (Hordeum vulgare L.). BMC Genom. 25, 846 (2024).

    Google Scholar 

  66. Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative CT method. Nat. Protoc. 3, 1101–1108 (2008).

    Google Scholar 

  67. Zhu, J. et al. Functional analysis on the role of HvHKT1.4 in barley (Hordeum vulgare. L) salinity tolerance. Plant Physiol. Biochem. 215, 061 (2024).

    Google Scholar 

  68. Tohge, T., de Perez Souza, L. & Fernie, A. R. On the natural diversity of phenylacylated-flavonoid and their in planta function under conditions of stress. Phytochem. Rev. 17, 279–290 (2018).

    Google Scholar 

  69. Chatterjee, T. K. & Fisher, R. A. Cytoplasmic, nuclear, and Golgi localization of RGS proteins: Evidence for n-terminal and rgs domain sequences as intracellular targeting motifs*. J. Biol. Chem. 275, 24013–24021 (2000).

    Google Scholar 

  70. Rogozin, I. B., Carmel, L., Csuros, M. & Koonin, E. V. Origin and evolution of spliceosomal introns. Biol. Direct 7, 11 (2012).

    Google Scholar 

  71. Li, H., Liu, G. & Xia, X. Correlations between recombination rate and intron distributions along chromosomes of C. elegans. Prog. Nat. Sci. 19, 517–522 (2009).

    Google Scholar 

  72. Wong, A., Gehring, C. & Irving, H. R. Conserved functional motifs and homology modeling to predict hidden moonlighting functional sites. Front. Bioeng. Biotechnol. 3, 82 (2015).

    Google Scholar 

  73. Matassi, G., Sharp, P. M. & Gautier, C. Chromosomal location effects on gene sequence evolution in mammals. Curr. Biol. 9, 786–791 (1999).

    Google Scholar 

  74. Prieto, P. Chromosome manipulation for plant breeding purposes. Agronomy 10, 1965 (2020).

    Google Scholar 

  75. Walden, N. & Schranz, M. E. Synteny identifies reliable orthologs for phylogenomics and comparative genomics of the Brassicaceae. Genome Biol. Evol. 15, evad034 (2023).

    Google Scholar 

  76. Ridout, K. E., Dixon, C. J. & Filatov, D. A. Positive selection differs between protein secondary structure elements in drosophila. Genome Biol. Evol. 2, 166–179 (2010).

    Google Scholar 

  77. Ren, Z., Ren, P. X., Balusu, R. & Yang, X. Transmembrane helices tilt, bend, slide, torque, and unwind between functional states of rhodopsin. Sci. Rep. 6, 34129 (2016).

    Google Scholar 

  78. Attwood, M. M. & Schiöth, H. B. Characterization of five transmembrane proteins: With focus on the Tweety, Sideroflexin, and YIP1 domain families. Front. Cell Dev. Biol. 9, 708754 (2021).

    Google Scholar 

  79. Ibraheem, O., Botha, C. E. J. & Bradley, G. In silico analysis of cis-acting regulatory elements in 5′ regulatory regions of sucrose transporter gene families in rice (Oryza sativa Japonica) and Arabidopsis thaliana. Comput. Biol. Chem. 34, 268–283 (2010).

    Google Scholar 

  80. Xiong, J. et al. Characterization of PtAOS1 promoter and three novel interacting proteins responding to drought in Poncirus trifoliata. Int. J. Mol. Sci. 21, 4705 (2020).

    Google Scholar 

  81. Lv, Y. et al. Identification of putative drought-responsive genes in rice using gene co-expression analysis. Bioinformation 15, 480 (2019).

    Google Scholar 

  82. Zhang, Y., Gao, P. & Yuan, J. S. Plant protein-protein interaction network and interactome. Curr. Genomics 11, 40–46 (2010).

    Google Scholar 

  83. Liu, H. et al. The Sugar Transporter family in wheat (Triticum aestivum. L): genome-wide identification, classification, and expression profiling during stress in seedlings. PeerJ 9, e11371 (2021).

    Google Scholar 

  84. Hao, S. et al. McWRI1, a transcription factor of the AP2/SHEN family, regulates the biosynthesis of the cuticular waxes on the apple fruit surface under low temperature. PLoS One 12, e0186996 (2017).

    Google Scholar 

  85. Theune, M. L., Bloss, U., Brand, L. H., Ladwig, F. & Wanke, D. Phylogenetic analyses and GAGA-motif binding studies of BBR/BPC proteins lend to clues in GAGA-motif recognition and a regulatory role in brassinosteroid signaling. Front. Plant Sci. 10, 466 (2019).

    Google Scholar 

  86. Challapa-Mamani, M. R. et al. Molecular docking and molecular dynamics simulations in related to leishmania donovani: an update and literature review. Trop. Med. Infect. Dis. 8, 1–13 (2023).

    Google Scholar 

  87. Bitencourt-Ferreira, G., Veit-Acosta, M. & de Azevedo, W. F. Hydrogen bonds in protein-ligand complexes. In Docking Screens for Drug Discovery 93–107 (2019).

    Google Scholar 

  88. Prabantu, V. M., Naveenkumar, N. & Srinivasan, N. Influence of disease-causing mutations on protein structural networks. Front. Mol. Biosci. 7, 1–11 (2021).

    Google Scholar 

  89. Alam, R. et al. GC-MS analysis of phytoconstituents from Ruellia prostrata and Senna tora and identification of potential anti-viral activity against SARS-CoV-2. RSC Adv. 11, 40120–40135 (2021).

    Google Scholar 

  90. Imon, R. R. et al. Natural defense against multi-drug resistant pseudomonas aeruginosa: cassia occidentalis L in vitro and in silico antibacterial activity. RSC Adv. 13, 28773–28784 (2023).

    Google Scholar 

  91. Hansson, T., Oostenbrink, C. & van Gunsteren, W. Molecular dynamics simulations. Curr. Opin. Struct. Biol. 12, 190–196 (2002).

    Google Scholar 

  92. Ouyang, L. et al. Genome-wide analysis of UDP-glycosyltransferase gene family and identification of a flavonoid 7-O-UGT (AhUGT75A) enhancing abiotic stress in peanut (Arachis hypogaea L.). BMC Plant Biol. 23, 626 (2023).

    Google Scholar 

  93. Ma, D., Sun, D., Wang, C., Li, Y. & Guo, T. Expression of flavonoid biosynthesis genes and accumulation of flavonoid in wheat leaves in response to drought stress. Plant Physiol. Biochem. 80, 60–66 (2014).

    Google Scholar 

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