JOURNAL ARTICLE

Gene silencing in broomrapes and other parasitic plants of the Orobanchaceae family: mechanisms, considerations, and future directions.

  • Published In: Journal of Experimental Botany, 2025, v. 76, n. 2. P. 243 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zainali, Nariman; Alizadeh, Houshang; Delavault, Philippe 3 of 3

Abstract

This article focuses on the use of RNA interference (RNAi) strategies, particularly host-induced gene silencing (HIGS), to control holoparasitic plants of the Orobanchaceae family, which cause severe damage to many crops and are difficult to manage by conventional means. It reviews the biology of these root parasites, especially broomrape species, highlighting key developmental stages and molecular targets suitable for gene silencing, such as genes involved in seed germination, haustorium formation, nutrient metabolism, and flowering. The article discusses various RNA silencing approaches—including hairpin RNAi (hpRNAi), virus-induced gene silencing (VIGS), artificial miRNAs (amiRNAs), and synthetic tasiRNAs (syn-atasiRNAs)—and emphasizes the importance of improving the delivery and long-distance movement of silencing signals from host plants to parasites. Additionally, it explores emerging technologies like nanoparticle-mediated delivery and miRNA-encoded peptides (miPEPs) as promising tools to enhance RNAi efficacy and suggests combinatorial gene silencing to prevent resistance development in parasites.

Additional Information

  • Source:Journal of Experimental Botany. 2025/01, Vol. 76, Issue 2, p243
  • Document Type:Article
  • Subject Area:Botany
  • Publication Date:2025
  • ISSN:0022-0957
  • DOI:10.1093/jxb/erae388
  • Accession Number:182369974
  • Copyright Statement:Copyright of Journal of Experimental Botany is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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