bioRxivMolecular BiologyPreprint
Identification of genes associated with productivity traits and salinity tolerance from activation tagged lines of rice
Kota Vamsee Raja, Kalva Madhanasekhar, Vudem Dashavantha Reddy, Attipalli Ramachandra ReddyiD, Khareedu Venkateswara Rao
Abstract
World-wide crop productivity is hugely impacted by diverse eco-environmental conditions. In the present investigation, activation tagged (AT) lines of rice endowed with improved agronomic attributes have been analyzed for tolerance to salinity stress besides identification of genes associated with these attributes. Under salinity stress conditions, AT lines exhibited increased seed germination rates, improved plant growth and development at vegetative and reproductive stages as compared to wild-type (WT) plants. Furthermore, AT lines disclosed enhanced plant water content, photosynthetic efficiency, stomatal conductance, water use efficiency and maximum quantum yield when compared to WT plants, leading to improved yields and delayed onset of stress symptoms. Moreover, AT lines revealed effective antioxidant systems causing decreased accumulation of reactive oxygen species and delayed salinity stress symptoms compared to WT plants. Reduced accumulation of malondialdehyde with concomitant increases in proline and soluble sugars of AT lines further endorsing their improved stress tolerance levels. TAIL and qRT-PCR analyses of AT lines revealed Ds element integrations at different loci and respective overexpression of identified candidate genes involved in various aspects of plant development and stress tolerance. Accordingly, the AT lines plausibly serve as a rare genetic resource for fortifying stress tolerance and productivity traits of elite rice cultivars. HighlightActivation tagged lines of rice endowed with improved agronomic attributes have been analyzed for tolerance to salinity stress besides identification and expression analysis of genes associated with these attributes.
- organism: plant (other)
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: experimental assay
- Citations
- 0
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