Plant biomass production is determined by total water used and water use efficiency (WUE, ratio of net CO₂ assimilation rate to the transpiration rate), especially under water-limited conditions (Passioura 1977). Despite demonstration of the relevance of these traits, efforts to improve them for crop improvement have been slow. The complex multi-gene regulation and difficulty in accurate measurement of these traits have been the major constraints. However, advent of stable isotope based techniques has significantly overcome these constraints. While carbon isotope discrimination (Δ¹³C) has been well established as a time averaged surrogate for WUE (Farquhar et al. 1989, Condon et al. 2004, Impa et al. 2005), we have provided experimental evidence for using oxygen isotope enrichment (δ¹⁸O) as an accurate estimate of Mean Transpiration Rate (MTR) on a season long scale (Bindumadhava et al. 1999, Sheshshayee et al. 2005).

Rice consumes over one half of the total irrigation water and efforts are on to reduce its water requirement though improvements in WUE. However, it can be visualized that enhancing WUE without substantially compromising for transpiration is crucial to improve productivity under a given condition. Exploitation of complex physiological traits such as transpiration and WUE can be effectively achieved if DNA markers tightly linked are identified. Although Δ¹³C has been adopted as a phenotyping strategy in efforts to associate QTLs in crops (Martin et al. 1989, Nadaradjan 2004, This et al. 2005), very few such attempts have been made to utilize δ¹⁸O as a phenotyping strategy to associate QTLs for transpiration rate.

In this study we adopted oxygen isotope enrichment technique to phenotype a RIL population (IR 50 x Moroberekan developed by Dr. S. Hittalamani) for differences in transpiration rate. Mean transpiration rate was assessed gravimetrically (Udayakumar et al. 1998, Impa et al. 2005) and δ¹⁸O in the leaf biomass was measured by pyrolysing 1 mg of leaf samples with glassy carbon catalysts at 1400°C using an elemental analyzer (TC/EA, Thermo electron, Bremen, Germany) interfaced with an isotope ratio mass spectrometer (Delta plus, Thermo electron, Bremen, Germany), working on continuous flow basis. The oxygen isotopic composition was determined with an analytical uncertainty less than 0.4‰ (Sheshshayee et al. 2005). A total of 320 random primers, 19 ISSR, 39 SSR primers and 8 AFLP primer combinations (EcoRI and MseI) were used to screen the parental DNA to identify the polymorphism. Genotypic data was generated with 94 RILs for all the polymorphic loci (206 loci). The segregating data for an individual locus was scored and its correlation with phenotypic values was assessed. Association of markers for the trait was done through variance analysis using MSTATC and association was claimed at a probability level of p<0.05.

Mean transpiration rate varied between 3.30 L. m^-2. day^-1 and 15.81 L. m^-2. day^-1 representing a significant genetic variability. δ¹⁸O also exhibited a significant genetic variability ranging from 25.40 ‰ to 36.94 ‰ among the RILs (Table 1). As predicted by theory δ¹⁸O showed a significant positive relationship with MTR (r = 0.3984; n= 94; p<0.001)

A total of 12 markers were found to be associated with MTR and the variations explained by these markers ranged from 4.08% to 17.27%. Further, 25 markers got associated with δ¹⁸O and marker OPT16_2 explained a highest variance of 18.39%. A total of four markers were found to be overlapping for MTR and δ¹⁸O (Fig. 1). These marker loci on an average explained 10.59% and 8.39% of the variance for δ¹⁸O and MTR, respectively. While this analysis revealed specific markers for MTR and δ¹⁸O, the overlapping markers can provide vital information about genomic regions that link δ¹⁸O with MTR. Further experiments are being conducted to validate these markers before employing them in Marker Assisted Selection for crop improvement.

References

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