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Analysis of Hop Stunt Viroid Diversity in Grapevine ( Vitis vinifera L.) in Slovakia: Coexistence of Two Particular Genetic Groups.

Peter AlaxinLukáš PredajňaAdam AchsZdeno ŠubrMichaela MrkvováMiroslav Glasa
Published in: Pathogens (Basel, Switzerland) (2023)
The hop stunt viroid (HSVd) is a widespread subviral pathogen infecting a broad spectrum of plant hosts including grapevine ( Vitis vinifera L.). Despite its omnipresence in virtually all grapevine growing areas around the world, molecular data characterizing HSVd populations are missing from Slovakia. Analysis of the complete nucleotide sequences of 19 grapevine variants revealed the existence of two genetic HSVd groups in Slovakia (internally named the "6A" and "7A" groups based on the particular stretch of adenines at nucleotide positions 39-44/45, respectively). Despite their sampling at different times in various unrelated vineyards, the 6A and 7A groups are characterized by low intra-group divergence (~0.3 and 0.2%, respectively). On the other hand, inter-group divergence reached 2.2% due to several mutations, seven of which were found to be group-specific and mainly (except for one) located in the region of the pathogenic domain. Interestingly, in addition to their frequent co-existence within the same geographical location, the mixed infection of the 6A and 7A type sequence variants was also unequivocally and repeatedly proven within single grapevine plants. The RNA secondary structure analysis of representative isolates from each of these two genetic groups indicated a potential compensatory explanation of such mutations. These group-specific sites could be pointing towards the evolutionary selection linked to the necessity of the viroid to retain its structural conformational integrity, crucial for its functional biochemical ability to interact with specific grapevine cellular host factors required for HSVd propagation.
Keyphrases
  • copy number
  • genome wide
  • single molecule
  • dna methylation
  • risk assessment
  • gene expression
  • molecular dynamics
  • electronic health record
  • machine learning
  • single cell