The Increased aspartate levels in transgenic cotton (Gossypium hirsutum L.) lead to improved tolerance against whitefly (Bemisia tabaci, Gennadius).
Ambreen GulAbdul Qayyum RaoMukhtar AhmedAyesha LatifAllah BakhshSehrish IftikharPublished in: Physiologia plantarum (2024)
The whitefly, a polyphagous insect pest feeding on nearly 1328 plant species, is a major threat to global cotton production and incurs up to 50% yield losses in cotton production in Pakistan. We investigated whether increased aspartate in phloem sap imparts whitefly toxicity and protects cotton plants from intense damage. The enzymatic step for aspartate production is carried through aspartate aminotransferase (AAT). In this study, we constitutively overexpressed the Oryza sativa cytoplasmic AAT (OsAAT2) under the CaMV35S promoter in Gossypium hirsutum cv. CIM-482. Real-time PCR analysis of the AAT transcripts revealed a 2.85- to 31.7-fold increase in mRNA levels between the different cotton lines. A substantial increase in the free-amino acid content of the major N-assimilation and transport amino acids (aspartate, glutamate, asparagine, and glutamine) was seen in the phloem sap of the transgenic cotton lines. The bioassay revealed that the two transgenic cotton lines with the highest free aspartate content in the phloem sap exhibited 97 and 94% mortality in the adult whitefly population and a 98 and 96% decline in subsequent nymph populations, respectively. There was also a significant change in the physiological behaviour of the transgenic cotton lines, with an increased net assimilation (A), gaseous exchange (Gs) and rate of transpiration (E). Improved morphological characteristics like plant height, total number of bolls and fiber yield were recorded in transgenic cotton lines. The AAT gene shows promise in mitigating whitefly infestations and enhancing the overall health and yield of cotton plants.
Keyphrases
- amino acid
- healthcare
- oxidative stress
- dna methylation
- mental health
- gene expression
- risk factors
- genome wide analysis
- mass spectrometry
- type diabetes
- young adults
- risk assessment
- machine learning
- genome wide
- single cell
- zika virus
- climate change
- hydrogen peroxide
- big data
- artificial intelligence
- health information
- aedes aegypti
- human health