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The Response of Chromosomally Engineered Durum Wheat- Thinopyrum ponticum Recombinant Lines to the Application of Heat and Water-Deficit Stresses: Effects on Physiological, Biochemical and Yield-Related Traits.

Gloria GiovenaliLjiljana KuzmanovićAlessandra CapoccioniCarla Ceoloni
Published in: Plants (Basel, Switzerland) (2023)
Abiotic stress occurrence and magnitude are alarmingly intensifying worldwide. In the Mediterranean basin, heat waves and precipitation scarcity heavily affect major crops such as durum wheat (DW). In the search for tolerant genotypes, the identification of genes/QTL in wild wheat relatives, naturally adapted to harsh environments, represents a useful strategy. We tested three DW- Thinopyrum ponticum recombinant lines (R5+, R112+, R23+), their control sibs lacking any alien introgression, and the heat-tolerant cv. Margherita for their physiological, biochemical and yield response to heat stress (HS) application at anthesis, also in combination with water-deficit stress applied from booting until maturity. Under HS, R5+ and R112+ (23%- and 28%-long 7el 1 L Th. ponticum chromosome segment distally inserted on DW 7AL, respectively) showed remarkable stability of the yield-related traits; in turn, R23+ (40%-long 7el 1 L segment), despite a decreased grain yield, exhibited a greater spike fertility index and proline content in spike than its control sib. Under water-deficit + HS, R5+ showed the highest increment in water use efficiency and in flag leaf proline content, accompanied by the lowest yield penalty even vs. Margherita. This research confirms the value of harnessing wild gene pools to enhance DW stress tolerance and represents a starting point for elucidating the mechanisms of Thinopyrum spp. contribution to this relevant breeding target.
Keyphrases
  • heat stress
  • genome wide
  • heat shock
  • genome wide identification
  • risk assessment
  • climate change
  • dna methylation
  • artificial intelligence
  • gene expression
  • bioinformatics analysis
  • deep learning
  • machine learning