GENOTYPIC VARIABILITY IN CARBON ISOTOPE
DISCRIMINATION AND WATER USE EFFICIENCY AMONG RECOMBINANT INBRED LINES OF SUNFLOWER
(Helianthus annuus L.)
Afifuddin Latif Adiredjo1, Philippe Grieu2
1Brawijaya University, Faculty of Agriculture, Department of Agronomy, Plant Breeding Laboratory, Malang 65145, Indonesia. Email: [email protected]
2Université de Toulouse, INP - ENSAT, UMR 1248 AGIR (INPT-INRA), Castanet-Tolosan 31326, France. Email: [email protected]
Abstract
To evaluate genotypic variability in carbon isotope discrimination (CID) and water use efficiency (WUE), a population of recombinant inbred lines (RILs) of sunflower (Helianthus annuus L.) was used.
78 sunflower RILs were grown under controlled conditions and measured some morpho – physiological traits including leaf area at flowering (LAf), net CO2 assimilation rate (A) and transpiration per day at flowering (Ef). WUE, called “potential” WUE (WUEp), was calculated as the ratio of assimilation potential (Ap) to transpiration per day at flowering (Ef) where Ap was derived from the multiplication of A with LAf. The CID was significantly varied among RILs and there was significant negative genetic correlation between CID and WUEp. Heritability of the CID was higher rather than the WUEp which reflected wide genetic variability of CID. CID can be proposed as an indicator to determine WUE as well as the selection criteria in plant breeding.
Keywords: genotypic variability, CID, WUE, sunflower
Introduction
Sunflower (Helianthus annuus L.) is one of the most important oil crops worldwide (Flagella et al., 2002). Upon this crop, study of genotypic variability affecting agronomic and economically important traits could help to improve breeding methods (Fick,1978; Fick and Miller, 1997; Kiani et al., 2009).
Genotypic variability of carbon isotope discrimination (CID) has received increasing attention as an indicator of water use efficiency (WUE) (Farquhar and Richards, 1984; Condon and Richards, 1993; Ehleringer et al., 1993; Adiredjo et al., 2014a and 2014b). During photosynthetic processes, C3
plants discriminate against the heavier carbon isotope (13C). Such a process is dependent on the ratio of intercellular to atmospheric carbon dioxide partial pressure (Pi/Pa), a higher CID resulting from higher Pi/Pa due to higher stomatal conductance. WUE is negatively associated with both Pi/Pa and CID (Farquhar and Richards, 1984; Ehleringer et al., 1993).
WUE could be defined in many ways, depending on the scale of measurement and the units of exchange being considered. For physiologists, the basic unit of production could be moles of carbon gained in photosynthesis (A) in exchange for water used in transpiration (T). For agronomists, the unit of production is much more likely to be the yield of harvested product achieved from the water consumption (Condon, 2004). However, direct measurement of WUE relies either on extensive leaf gas-exchange data or long-term measurements of plant water consumption and biomass production.
Therefore, a population of 78 RILs was used to evaluate genotypic variability as well as genetic correlations of CID and WUE. The WUE was an estimated-WUE, called “potential” WUE (WUEp), by using the traits of net CO2 assimilation rate (A), leaf area at flowering (LAf) and transpiration per day at flowering (Ef). The objectives of this paper were: (i) to analyze genotypic variability of CID and (ii) to evaluate genetic correlations between CID and WUE in sunflower genotypes. We are interested in exploring the possibility of using CID as an indicator to select sunflower genotypes with high WUE.
Materials and Methods
Plant material and traits measurement
The sunflower genotypes used in this study were those of Kiani et al. (2007) and Kiani et al.
(2009). The bulk of the phenotypic traits were presented in the previous study, but the CID reported here was not available at the time of the previous study. A population of 78 recombinant inbred lines (RILs) derived from a cross between PAC2 and RHA266 was generated as described previously by Kiani et al. (2009). Briefly, plants were grown in the greenhouse under controlled conditions.
Temperature was maintained at 25/18 ± 2_C (day/night) and relative humidity was about 65–85 ± 5%.
Supplementary light giving an approximately 16-h light and 8-h dark period was maintained during experiment. Plants were individually grown in plastic pots (4.0 l) containing a mixture of 40% soil, 40%
compost and 20% sand.
Measurement of net CO2 assimilation rate (A) was made at light-saturation of 1.500 mol m –2 s–1 photosynthetic photon flux density (PPFD), ambient CO2 concentration(approximately 400
mol mol–1), leaf temperature controlled at 25 ± 20C and with relative humidity of 60 ± 5%. Leaf area at flowering (LAf) was measured at flowering stage with the calculation following formula: LAf = ∑ 0.7 L X W (Alza and Fernandez-Martinez, 1997). Potential of net CO2 assimilation per plant (Ap) was derived from the multiplication of A with LAf. Transpiration per day at flowering (Ef) was measured by weighing the pots every day during 12 days at flowering period, the difference of water loss between two days was considered as daily transpiration. The “potential” water use efficiency (WUEp) was calculated as the ratio of Ap to Ef.Carbon isotope discrimination (CID) analysis
Oven-dried leaves of each plant were ground into a homogenous fine powder and 2-3 mg subsamples were weighed and placed in capsules (Elemental Microanalysis, UK). All samples were organized into a clean 96-well tray of a microplate to be analyzed using a continuous flow Isotope Ratio Mass Spectrometer (Sercon Ltd., Cheshire, UK) at UC Davis Stable Isotope Facility (California, USA). Carbon isotope composition
(
13C) was calculated relative to the international Pee Dee Belemnite (PDB) standard (Farquhar et al., 1989):
plant = (Rsa – Rsd)/Rsd X 1000[‰] where Rsaand Rsdare the 13C : 12C ratios of the sample and the standard, respectively (Craig, 1957). CID, a factor related to isotope fractionation by the photosynthetic process relative to the source carbon was estimated as: CID =
(
air–
plant)/(1 +
plant/1000)
where
air is the 13Ccomposition of atmospheric CO2, which is assumed to be – 8.0 ‰ (Farquhar et al., 1989).Statistical analysis
In the present study, the software of statistical package PASW statistics 18 (IBM, New York, USA) was used. The test of normality was determined by Kolmogorov-Smirnov test (lilliefors correction) of the package which completed with histogram and normal Q-Q plot graph. For each analysis, trait was considered as dependent variable, genotype and block were considered as fixed factors. Tests were performed two-sided with a significance level of 5 %. Tukey-test was used to know the differences among genotypes.
Genetic (rg) and phenotypic (rp) correlations between two traits were calculated using variance components as follows: rg = Covg (xy) /(g (x) g (y))0.5 and rp = Covp (xy) /(p (x) p (y))0.5 where Covg
is the genetic covariance between traits x and y, Covpis the phenotypic covariance between traits x and y. gis the genetic variance and p is the phenotypic variance (Kearsey and Pooni, 1998).
Broad-sense heritability (h2) derived from the ratio of genetic variance to phenotypic variance among genotypes (h2 =g/p). Genetic variance (g) and phenotypic variance (g) can be estimated from the analysis of variance considering genotypic effects as random. Phenotypic variance was come from combination of genetic variance with environmental variance (p = g + e) where e is the environmental variance.
Result and discussion
Genotypic variability and heritability (h2) for CID and related traits
The CID values ranged from 18.54 to 26.58‰ and the mean value was 21.95‰ (Table 1).
Estimates of heritability of CID was high and more than 50% (h2 =0.82). The result of analysis of variance (ANOVA) of mean squares for CID and other related traits are shown in Table 2. The CID was significantly different among RILs (p < 0.01). The CID values varied from 18.54 to 26.580/00. This result is in agreement with the argument of Jones (1992) that the range value of CID for C3 plant material was from 13 to 280/00. A large range of CID variation (8.040/00) was observed in our study, exceeding the range of about 2.50/00 – 4.4‰ investigated in greenhouse studies of sunflower (Virgona and Farquhar, 1996; Lambrides et al., 2004). The large ranges and the significant difference of CID among RILs in the present study suggested that there is possibility to conduct the selection program for sunflower genotypes by using CID as a trait.
In the present study, a high heritability of CID indicated that the genetic effect for CID was dominant rather than the environmental effect, and the assay developed for pots grown sunflower was robust. The similar results have been reported by Lambrides et al. (2004) where the heritability was high among 87 genotypes and 20 inbred lines of sunflower. Several works showed that CID had higher heritability compared to the WUE and yield, for example as reported in wheat (Ehdaie et al., 1991), bean (Zacharisen et al., 1991), cotton (Stiller et al., 2005). Hence, the higher heritability compared to WUEp suggested that CID can be used as a selection tool in breeding strategies to improve WUE.
Genetic correlation between CID and WUEp and other related traits
There was significant negative genetic correlation between CID and WUEp as well as between CID and A and/or Ap (Table 3). A negative genetic correlation between CID and WUEp suggests possibility of employing WUEp as surrogate of WUE at whole-plant level. Such negative genetic correlation is in accordance with Lambrides et al. (2004) that observed a negative genetic correlation (rg = -0.92**) between CID and WUE of sunflower genotypes in greenhouse experiment at well- watered condition. Indeed, this result is consistent with an argument of Farquhar et al., 1984, studied on wheat, and is in accordance with numerous authors (Lauteri et al., 1993; Li et al., 1999; Frank and Berdahl, 2001; Lambrides et al., 2004; Zhao et al. 2004). This significant genetic correlation indicated that CID is a potential indicator to determine WUE in sunflower genotypes under optimal plant growth (non-limited soil water availability).
The net CO2 assimilation (A) and the potential of net CO2 assimilation (Ap) were genetically associated with CID, since the negative genetic correlations were observed among these traits.
Another study on Coffea species grown in greenhouse and watered daily with enough water has shown a strong negative correlation between CID and A (Carelli et al., 1999). This result might be explained that high amount of carbon isotope composition (13C) which cause little discrimination of
13C was significantly maintained the rates of photosynthetic CO2 fixation. Mott (1997) noted that to sustain reasonable rates of CO2 fixation, plant must make an enormous amount of Rubisco.
Study of genetics for the traits that have significant genetic correlation in this paper is an important aspect throughout breeding, physiological and agronomical approach in sunflower development. The CID, WUEp, A, Ap and LAf are most likely to be polygenic, since any gene that affects either assimilation rate per unit leaf area or stomatal conductance can have an effect.
Farquhar et al. (1989) proposed that the genetic analysis of a polygenic trait like A may enable breeders to follow the results of backcrossing material with desirable A into commercial cultivars but in parallel with pursuing research on the genetic control of CID by the plant. In fact, the variation of CID and its related traits are considered by the influences of genetic and environmental (water availability, light intensity, nutrition, etc) effects.
Conclusion and suggestion
Our work proved that there was wide genetic variability for CID, the heritability of the CID was higher rather than the WUEp and the CID was significantly associated with the WUEp. These results suggest possibility of using CID as an indicator to determine WUE in sunflower.
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TABLE
Table 1. Range of genotypic variability and heritability (h2) for CID and related traits of 78 RILs.
Trait CID WUEp A Ap Ef LAf
Mean 21.95 206.17 19.56 50551.38 246.46 2563
Minimum 18.54 114.76 14.83 27521.48 147.89 1528
Maximum 26.58 274.51 25.03 76583.89 324.48 3505
Std.dev. 2.15 36.49 2.72 12845.23 49.52 529.86
h2 0.82** 0.01 0.01 0.28* 0.68** 0.58**
CID = carbon isotope discrimination (0/00), WUEp= “potential” water use efficiency (mol CO2 .cm2. m-2 s-1 ml-1 day-1), A = net CO2
assimilation rate (mol CO2 m-2 s-1), Ap = potential of net CO2 assimilation per plant (mol CO2 .cm2. m-2 s-1), Ef = plant transpiration per day at flowering period (ml/day), LAf = leaf area at flowering (cm2).
**) high value of heritability (0.5 < h2): the genetic effect was dominant rather than the environmental effect. *) medium value of heritability (0.2 < h2 < 0.5): the genetic and the environmental effects were equally involved (Standsfield, 1969).
Tabel 2. Result of analysis of variance (mean squares) for CID and related traits of 78 RILs.
SV d.f. CID WUEp A Ap Ef LAf
Genotype 77 11.62** 3996 ns 22.15 ns 489727937** 7357** 842264ns
Block 2 11.94** 58218** 302.06** 7082396692** 28791** 6019183**
Error 154 1.04 3787 21.30 225765657 1008 224591
Total 233
SV = source of variance, d.f = degree of freedom
*) significant at P < 0.05, **) siginificant at P < 0.01, ns) not significant
CID = carbon isotope discrimination (0/00), WUEp= “potential” water use efficiency (mol CO2 .cm2. m-2 s-1 ml-1 day-1), A = net CO2 assimilation rate (mol CO2 m-2 s-1), Ap = potential of net CO2 assimilation per plant (mol CO2 .cm2. m-2 s-1), Ef = plant transpiration per day at flowering period (ml/day), LAf = leaf area at flowering (cm2).
Table 3. Genetic correlation (rg) between CID and WUEp and related traits of 78 RILs.
Trait (x) Trait (y) rg (xy)
CID WUEp -0.30*
A -0.54*
Ap -0.27*
Ef -0.22
LAf -0.18
*) Significant at at P < 0.05, values without * are not significant
CID = carbon isotope discrimination (0/00), WUEp= “potential” water use efficiency (mol CO2 .cm2. m-2 s-1 ml-1 day-1), A = net CO2 assimilation rate (mol CO2 m-2 s-1), Ap = potential of net CO2 assimilation per plant (mol CO2 .cm2. m-2 s-1), Ef = plant transpiration per day at flowering period (ml/day), LAf = leaf area at flowering (cm2).