Effect of Water Stress on Rapeseed Cultivars Using Morpho-Physiological Traits and Their Relations with ISSR Markers

Document Type : Research Paper

Authors

1 University of Mohaghegh Ardabili, Ardabil, Iran

2 Mohaghegh Ardabili, Ardabil, Iran

Abstract

Abstract
To study the effect of water stress in rapeseed cultivars at the seedling stage, 10 rapeseed cultivars were evaluated at three irrigation levels [normal irrigation (control) and irrigation after depletion of 60 and 85% of available soil water]. Analysis of variance showed considerable variation among cultivars. Water stress reduced all of the studied morphological characteristics, especially shoot and root dry weight, root volume and increased chlorophyll content and chlorophyll fluorescence. Cluster analysis at three levels of irrigation regime, assigned cultivars in different groups. Cultivars Licord, Opera and SLM043 were grouped together and showed higher average for all traits compared with other cultivars at all of the irrigation conditions. ISSR analysis using 11 primers produced 54 polymorphic bandsin the studied cultivars. Mean PIC and MI of all primers were 0.21 and 1.03, respectively. Cluster analysis based on molecular data using Nei's genetic distance assigned the cultivars into three clusters. Associations between molecular markers and morpho-physiological traits, were assessed by stepwise multiple regression analysis at different stress levels. The highest amount of variation contributed by ISSR markers belonged to relative leaf water content (78%) at non-stress condition, to root/shoot index (66%) at moderate stress condition and to root length (53%) at severe stress condition.
 

Keywords


Introduction

Oil seeds are the second source of food after cereals. Rapeseed (Brassica napus L.) is an important agricultural crop grown primarily for its edible oil. The meal that remains after oil extraction has value as a source of protein for the livestock feed industry (Jensen et al. 1996). According to FAO (2009), rapeseed is the third most important source of oil seeds crop in the world after soybean and palm oil. Rapeseed contains about 40-44 % oil and is one of the major oilseed crops that grown profitably in rotation with wheat (Carmody 2001). Because of high water use efficiency, drought tolerance and also moderate tolerance to saline soil conditions, rapeseed has a special position for production in arid regions (Nielson 1997; Albarrak 2006).

Water shortage is the most significant factor restricting plant growth and crop productivity in majority of the agricultural fields of world (Tas and Tas 2007). The production of the rapeseed plant is limited by soil salinity and water shortage. Therefore, development of varieties with increased salinity and drought tolerance is important for growing this economical plant in regions where water is limited. Germination may occur in soils with low water content (Anastasi et al. 2003). However, this may cause delayed and reduced germination and seedling growth, with negative effects on crop establishment, crop-weed competition and final grain yield (Willenborg et al. 2004; Andalibi et al.  2005).

DNA markers have been valuable in crop breeding, especially in studies on genetic diversity and gene mapping. The commonly used polymerase chain reaction (PCR)-based DNA marker systems are random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP) and simple sequence repeats (SSRs) or microsatellites (Staub et al., 1996; Gupta and Varshney 2000). The major limitations of these methods are low reproducibility of RAPD, high cost of AFLP and the need to know the flanking sequences to develop species specific primers for SSR polymorphism. ISSR-PCR (inter simple sequence repeat) is a technique that overcomes most of these limitations (Meyer et al. 1993; Wu et al. 1994; Gupta et al. 1994; Zietkiewicz et al. 1994). The technique combines most of the benefits of AFLP and microsatellite analysis with the universality of RAPD. ISSRs have high reproducibility, possibly due to the use of longer primers (16–25 mers) as compared to RAPD primers (10-mers) which permits the subsequent use of high annealing temperature (45–60ºC) leading to higher stringency. ISSRs have been proposed as a new source of genetic markers, which overcome the technical limitation of restriction fragment length polymorphisms (RFLP) (Ibrahim et al. 2011). ISSRs have been successfully used in the study of genetic diversity at inter and intra specific level in a wide range of crop species including rice (Joshi et al. 2000), cotton (Liu and Wendel 2001; Sharaf et al. 2009), rapeseed (Wakui et al. 2009). Molecular markers associated with quantitative trait loci (QTL) for drought adaptive traits could greatly enhance progress in breeding for drought tolerance. Molecular markers improve the efficiency of breeding by allowing manipulation of the genome through marker-assisted selection (Ibrahim et al. 2011). Therefore, in this investigation, some morpho-physiological traits and their relations with ISSR markers in 10 rapeseed cultivars were studied under water stress conditions.

 

Material and Methods

In this study, 10 cultivars of rapeseed (Brassica napus L.) (Opera, Adder, SLM043, SLM046, Elvis, Okapi, Elite, Ebonit, Orient and Licord) were grown in a factorial experiment based on randomized complete block design with three replications in a controlled greenhouse (20±3ºC temperature of day, 16±3ºC temperature of night, 16/8h day/night photoperiod) under different irrigation conditions. Irrigation levels were full irrigation (control) and irrigation after depletion of 60 and 85% of available soil water.

Before planting, 100 cm3 volumes of undisturbed soil samples were taken from four pots. Samples were oven dried at 105ºC and bulk density was calculated from cylinder volume and dry soil mass (Jacob and Clark 2002).

Field capacity of a soil is the approximate water content at which the internal drainage of water through the soil profile due to gravity becomes negligible. The method for determination of water content at field capacity ( ) consisted of heavy watering of pots and monitoring the soil moisture variation over time until the moisture of soil clearly tended to converge to a certain value taken as  (Cavazza et al. 2007). During the monitoring of soil moisture, the top surface was covered with black plastic to prevent evaporation.

Particle size distribution was measured by using hydrometric method (Jacob and Clark 2002). Wilting point was estimated using the ROSETTA software (Schaap et al. 2001). Crop coefficients were obtained from FAO-56 tables (Allen et al. 1998). Relative humidity in the greenhouse and the amount of evaporation from class A pan (Epan) was recorded daily and reference evapo-transpiration ( ) was obtained using pan coefficient.  Available water (AW), crop water requirement ( ) and irrigation frequency (I) were calculated based on Allen et al.  (1998).

After depletion of 60 and 85% of available soil water, pots were irrigated to the field capacity level regarding the root depth at each growth stage.

In the control treatment, plants were irrigated based on their need from planting to harvest.

 Two weeks after water stress treatment, chlorophyll and fluorescence indices were measured with Chlorophyll Meter SPAD-502 (Konica Minolta) and Chlorophyll Fluorometer OSI-30 (ADC Bioscietific), respectively. Root area was calculated by the following formula:

 

Root area=2*{(Root volume)*3/14*(Root length)} 0/5

Relative water content (RWC) was obtained by floating the leaf discs (five discs from each leaf with a 50 mm diameter) on distilled water for 24 hours at 4°C under dim light. Then, the turgid weight (TW) was determined after floating and the dry weight (DW) was obtained after the samples were dried for 24 hours at 70°C. Fresh weight (FW), TW and DW were used to calculate RWC as follows (Barrs and Weatherly 1962):

RWC= (FW-DW)/(TW-DW)

The DNA of leaf samples from 6 plants of each cultivar were extracted separately using the CTAB procedure according to Saghai-Maroof et al. (1984). The quality and quantity of DNA samples were assessed using spectrophotometer and 0.8 percent agarose gel electrophoresis. All of the DNA samples were diluted to 25ng/μl and used in PCR reactions. Thirty four ISSR primers [from Bioneer Company (South Korea)] were used to study polymorphism in the plants and polymorphic primers were used for genotyping.

PCR reaction for ISSR primers was performed in a volume of 18μl containing 25 ng of DNA template, 2.22 mM MgCl2, 0.11 mM each dNTPs, 0.44 μM primer, 1U Taq DNA polymerase and 1x PCR buffer. Amplification was programed for 5-min initial denaturation step at 94oC followed by 35 cycles of denaturing at 94oC for one min, annealing at 49-60oC for one min, extension step at 72oC for one min and a final extension step at 72oC for 5 min. Amplification products were separated by 1.5% (w/v) agarose gel electrophoresis and were stained with ethidium bromide. ISSR bands were scored manually as present (1) or absent (0). Data were analyzed using SPSS 16, GenAelex 6.4, NTSYS 2.2 and PopGen32 software.

Results and Discussion

Physical properties of the soil were as follows: soil texture was clay loam; bulk density (g.cm-3) was 0.9; water content at field capacity (g.g-1) was 0.39 and wilting point moisture (g.g-1) was 0.14.

The results showed that water stress had significant effect on all of the traits (P ≤0.01). Significant differences were also observed among cultivars (Table 1). Water stress decreased shoot and root dry weight, root length, volume and area, number of leaves, root/shoot index and relative water content but increased SPAD and Fv/Fm (Table 2). It seemed that increased chlorophyll content caused decrease in light repression and increase in Fv/Fm. The Fv/Fm could be used as a physiological index for selecting osmotic stress tolerant cultivars (Paul Parkhill et al. 2001).

Licord and SLM043 cultivars had the highest shoot dry weight. Licord was superior for most of the traits (Table 3). Opera showed the highest root dry weight, root length and chlorophyll content. However, its shoot dry weight, number of leaves, root/shoot index and chlorophyll fluorescence were less than those of Licord. Among the studied cultivars, Licord and Ebonit showed the highest and the lowest number of leaves, respectively. Although water stress reduced the shoot dry weight, number of leaves and RWC in different cultivars, it was observed that Opera and Licord had relatively better performance under water stress conditions, because, they had higher root dry weight, root length, chlorophyll content and chlorophyll fluorescence.

Grouping the studied cultivars by UPGMA cluster algorithm using Euclidean distance is shown in Figure 1. In the normal condition, the first group consisted of Elite, Ebonit, Orient, Opera and SLM046. Elvis, Okapi and SLM043  were placed in another cluster. The mean of these clusters showed the lowest and the highest deviations from the total mean, respectively.

Three distinct clusters at 60% AW (irrigation after depletion of 60% of available soil water) were obtained. The third and first clusters had the lowest and the highest deviations from the total mean, for most of the studied traits, respectively. Opera, SLM043, SLM046 and Licord were located in the second cluster (Figure 1).

Two clusters were identified at 85% AW (irrigation after depletion of 85% of available soil water). Group 1 showed the highest deviation from the total mean, for most of the studied characters. SLM043, Opera, Elvis and Licord were located in the first cluster. Elite, Ebonit and Orient were located in the groups with low averages at the control and stress conditions. Therefore, these cultivars did not show better performance in this experiment. Adder was located in the group with higher means at the normal condition but located in the group with lower averages at the water stress conditions. This variety was not tolerant to the drought stress. Elvis was placed in groups with lower averages under the control and moderate stress conditions, but located in the group with higher averages at the severe water stress regime. Opera was located in the group with lower trait averages at the normal condition while at the water stress environments it was placed in the group with highest average values. This means that Opera was tolerant to water stress. Licord and SLM043 were  located  in  the  group  that  had higher trait

 

 


Table 1. Analysis of variance for studied traits of rapeseed cultivars at normal and water stress conditions

Source of variation

df

Mean squares

Shoot dry weight

Root dry weight

Root length

Root volume

Root area

Number of leaves

Root/

Shoot

Chlorophyll content

Relative water content

Chlorophyll fluorescence

Replication

2

2.17ns

0.14**

1.57ns

225.81**

1029.64**

0.7ns

0.002ns

5.72ns

58.38ns

0.0001ns

Water  (W)

2

757.6**

10.34**

603.18**

15340.54**

105062.7**

204.4**

0.048**

416.56**

8179.28**

0.033**

Variety (V)

9

1.77*

0.04*

82.13**

21.35ns

218.62ns

1.75**

0.009**

39.54**

42.11ns

0.0007*

W*V

18

0.7ns

0.016ns

41.31ns

7.95ns

161.96ns

0.61ns

0.002ns

12.27ns

65.8ns

0.0002ns

Error

58

44.55

1.19

29.64

33.92

178.56

0.63

0.002

10.51

36.79

0.0003

C.V

 

15.91

13.1

14.53

30

15.64

10.93

19.37

6.03

9.69

2.16

ns, * and ** are non-significant and significant at 0.05 and 0.01 p values, respectively.

                         

 

 

Table 2. Means of irrigation levels for the studied rapeseed traits

Water condition

Shoot dry weight (gr)

Root dry weight (gr)

Root length (cm)

Root volume (cm3)

Root area (cm2)

Number of leaves

Root/Shoot

Chlorophyll content(SPAD)

Relative water content (%)

Chlorophyll fluorescence (ms)

Normal

11.17a

1.76a

42.61a

45.43a

153.18a

10a

0.27a

51.03b

7.32a

0.77c

60% AW

3.78b

0.87b

34.23b

8.26b

59.03b

7b

0.21b

58a

63.92b

0.82b

85% AW

1.59c

0.68c

35.23c

4.53c

44c

4.8c

0.28a

52.23b

45.38c

0.83a

Means with the same letter in each column are not significantly different at 0.05 probability level using Duncan's multiple test

 

 

Table 3. Means of measured traits in the rapeseed cultivars under study

 

Varieties

Shoot dry weight (gr)

Root dry weight (gr)

Root length (cm)

Number of leaves

Root/Shoot

Chlorophyll content(SPAD)

Chlorophyll fluorescence (ms)

Opera

5.33

1.15

40.77

7.33

0.29

56.62

0.816

Adder

5.64

0.97

39.11

7.66

0.21

52.22

0.817

SLM043

6.13

1.1

38.77

7.33

0.23

53.82

0.81

SLM046

5.29

1.14

36.66

6.88

0.27

54.35

0.8

Elvis

5.24

1.14

37.05

7.77

0.28

54.23

0.823

Okapi

5.2

1.09

35.27

7.11

0.26

52.72

0.814

Elite

5.42

1.01

34.66

7.11

0.22

55.61

0.8

Ebonit

5.24

1.11

33.05

6.66

0.28

50.62

0.8

Orient

5.1

1.02

36.05

6.77

0.25

51.04

0.821

Licord

6.44

1.17

43.11

8

0.3

56.34

0.821

LSD (5%)

0.82

0.13

5.13

0.74

0.047

3.06

0.016

 

 

 

   

A

   

B

   

Figure 1. Grouping of 10 rapeseed cultivars using cluster analysis based on UPGMA method with the average deviation of group means from the grand mean of the traits under study. A (Normal), B (60% AW) and C (85% AW).

 


averages than the other groups at both water stress and non-stress conditions. These cultivars had, also, the largest distance from Elite, Ebonit, and Orient cultivars for evaluated traits. These cultivars could be crossed to produce the base populations for genetic studies and selection programs.

From 34 ISSR primers used in this study, 11 primers showed polymorphisms and produced 64 bands in total. Of these, 54 and 10 were polymorphic and monomorphic, respectively (Table 4). Figure 2 shows P8 primer banding pattern in the studied cultivars. P5, P11 and P13 primers had the highest polymorphism (100%). Charters et al. (1996) using three 5′ anchored primers together could distinguish 20 cultivars of Brassica napus.

P5 and P22 revealed the highest PIC (0.22) and MI (1.72), respectively (Table 4). PIC provides the value of a marker for detecting polymorphism introduced by Botstein et al. (1980). Marker index shows the potential of each primer in the production of more bands (Anderson et al. 1993; Powell et al. 1996). Chadha and Gopalakrishna (2007) reported PIC values of 0.1 to 0.5 and MI index of 1.54 in the study of genetic diversity using ISSR markers in rice.

The highest (0.4) and the lowest (0.21) amounts of mean genetic diversity within cultivars, based on Nei's gene diversity (Nei 1978), were found in Orient and Adder, respectively (Table 6). The total (HT = 0.341), within (HS = 0.221) and the average gene differentiation among the varieties over all loci (GST=0.34) indicated that there was a good genetic variation within and between the studied rapeseed varieties.

Figure 2 shows the grouping of the rapeseed cultivars based on molecular data using UPGMA cluster analysis method and Nei's genetic distance. The cluster analysis separated the rapeseed cultivars into three clusters. The first cluster consisted of five cultivars: Opera, Adder, SLM043, SLM046 and Elite. Four cultivars, Okapi, Ebonit, Orient and Licord were clusterd together and Elvis was located alone in the third cluster. These results had some similarities with the grouping based on morpho-physiological data.

Based on regression analysis, in total 23 and 30 ISSR markers were associated with the measured traits at the three irrigation levels (Tables 6 and 8). There was only one marker related to shoot dry weight, root area, chlorophyll content, RWC and R/S at the normal irrigation level, whereas three markers were associated with the root dry weight and chlorophyll fluorescence. Positive markers related to RWC could explain 78% of total variance of this trait. R/S related markers could explain 21% of the total variance (Table 5). At 60% AW three markers were associated with root length, number of leaves and RWC, and four markers were associated with shoot dry weight and root area. Markers in association with R/S explained 66% of the variation, whereas amount of explained variance by positive markers for chlorophyll content was 23%. At 60% AW condition, P11M2 marker was the most effective marker associated with studied traits (Table 6). At 85% AW, four markers were associated with chlorophyll fluorescence. Markers in association with root length explained 53% of

 

 

   

Figure 2.Grouping of the cultivars using molecular data and UPGMA cluster analysis method based on Nei’s genetic distance (left) and banding pattern of ISSR-8 primer (right). (V: variety and R: repeat).

 

 

Table 4. Primers sequence, total number of amplicons, monomorphic amplicons, polymorphic amplicons and percentage of polymorphism, as revealed by ISSR analysis

Primer

Sequence

Total Amp

Mono Amp

Poly  Amp

Polymorphism%

      PIC

  MI

P1

5' AGAC AGACGC 3'

6

2

4

66.6

0.222

0.88

P3

5' AGAGAGAGAGAGAGAGC 3'

6

1

5

83.3

0.203

1.01

P5

5' AACAACAACGC 3'

6

0

6

100

0.225

1.35

P8

5' GACGACGACGACG 3'

8

2

6

75

0.203

1.21

P11

5' GTGGTGGTGGC 3'

3

0

3

100

0.192

0.57

P12

5' TTGTTGTTGTTGTTGC 3'

5

1

4

80

0.197

0.98

P13

5' ACACACACACACACACYG 3'

7

0

7

100

0.215

1.5

P15

5' ACGACGACGACGAAC 3'

4

1

3

75

0.214

0.64

P16

5' CACACACACACAAG 3'

4

1

3

75

0.203

0.61

P22

5' ATGATGATGATGATGATG 3'

9

1

8

88.8

0.215

1.72

P32

5' AGAGAGAGAGAGAGAC 3'

3

1

2

66.6

0.218

0.87

 

Total

61

10

51

 

 

 

 

Average

5 54

0.9

4.6

82.7

0.21

1.03

Mono= Monomorphic

Poly= Polymorphic

Amp= Amplicons

 

 

 

 

 

Table 5. Mean genetic diversity within cultivars based on Nies (1978) gene diversity coefficient

Cultivar

Genetic diversity

Cultivar

Genetic diversity

Opera

0.25

Okapi

0.33

Adder

0.21

Elite

0.38

SLM043

0.24

Ebonit

0.36

SLM046

0.29

Orient

0.4

Elvis

0.36

Licord

0.37

 

 

Table 6. Regression coefficients and adjusted R2 for the multiple regression of the morpho-physiological traits with ISSR marker at the normal condition

 

Shoot dry weight

Root dry weight

Root length

Root volume

Root area

Number of leaves

Root/Shoot

Chlorophyll content

Relative water content

Chlorophyll fluorescence

Intercept

28.5

17.5

53

76.5

220.69

14.38

0.51

52.46

44.52

0.77

P1M1

 

 

 

 

0.313

 

 

 

 

 

P3M1

 

-0.342

 

-0.322

 

 

 

 

 

 

P8M4

 

 

 

-0.375

 

 

 

 

 

 

P8M5

 

 

 

 

 

0.639

 

0.533

 

 

P11M2

 

 

-0.304

 

 

 

 

 

 

 

P11M3

 

 

-0.608

 

 

 

 

 

 

 

P13M1

 

 

 

 

 

-0.339

 

 

 

 

P13M6

 

-0.372

 

 

 

 

 

 

 

 

P16M1

-0.354

 

 

 

 

 

 

 

 

0.674

P16M2

 

 

 

 

 

 

 

 

 

-0.352

P22M1

 

 

 

 

 

 

 

 

-0.642

 

P22M8

 

0.406

 

 

 

 

-0.352

 

 

 

P32M3

 

 

 

 

 

 

 

 

 

-0.357

R2

 

0.399

0.54

0.591

0.23

0.417

0.211

0.698

0.787

0.405

 

 

 

 

 

the variation whereas amount of explained variance for number of leaves was 33%. At 85% AW, P13M6 marker was the most effective marker associated with the traits under study (Table 7).

DNA molecular markers are important tools which can be incorporated in these kinds of analyses. There is not enough marker data in rapeseed to screen rapeseed genotypes tolerant to water stress.

 

Conclusion

The plant performance of rapeseed cultivars reduced significantly under water stress, but some traits such as root/shoot ratio, chlorophyll content and chlorophyll fluorescence increased under this condition. These traits may be used for indirect selection of high yielding rapeseed genotypes under water deficit condition. Licord, SLM043 and Opera were more tolerant to water deficit stress and had, also, the largest distance from Elite, Ebonit and Orient. These cultivars could be crossed to produce the base populations for genetic studies, selection programs and producing mapping populations. Furthermore, several ISSR markers were related with some rapeseed traits that may be used in QTL mapping programs. In addition, ISSR markers revealed relatively high genetic diversity for the rapeseed cultivars under study which can be utilized in the breeding programs.

 

 

 

Table 7. Regression coefficients and adjusted R2 for the multiple regression of the morpho-physiological traits with ISSR markers at the 60% AW stress condition

 

Shoot dry weight

Root dry weight

Root length

Root volume

Root area

Number of leaves

Root/Shoot

Chlorophyll content

Relative water content

Chlorophyll fluorescence

Intercept

7.29

2.49

34.17

21.19

97.58

9.6

0.245

60.34

30.15

0.83

P1M1

-0.433

 

 

-0.38

-0.31

 

 

 

 

 

P3M1

 

 

0.331

 

 

 

 

 

 

 

P5M2

 

 

0.397

 

 

 

 

 

 

-0.343

P8M3

 

 

 

 

 

 

 

 

0.455

 

P8M5

 

0.353

 

 

 

 

0.469

 

-0.781

 

P11M2

 

 

0.471

 

0.426

 

0.426

 

-0.371

 

P11M3

 

 

 

 

0.352

 

 

 

 

 

P13M1

-0.338

 

 

 

 

 

 

 

 

 

P13M5

 

 

 

 

 

 

 

 

 

0.423

P13M6

 

 

 

 

-0.436

 

 

 

 

 

P15M1

 

 

 

 

 

-0.355

 

 

 

 

P15M3

0.375

 

 

 

 

 

 

0.392

 

 

P22M1

0.457

 

 

 

 

0.41

 

 

 

 

P22M5

 

 

 

 

 

0.34

 

 

 

 

R2

0.391

0.434

0.49

0/3

0.544

0.392

0.661

0.238

0.649

0.431

 

 

 

Table 8. Regression coefficients and adjusted R2 for the multiple regression of the morpho-physiological traits with ISSR markers at the 85% AW stress condition

 

Shoot dry weight

Root dry weight

Root length

Root volume

Root area

Number of leaves

Root/Shoot

Chlorophyll content

Relative water content

Chlorophyll fluorescence

Intercept

5.03

1.67

34.55

18.4

87.66

9.59

0.4

61.81

28.69

0.84

P3M2

 

 

 

 

 

 

 

 

-0.346

 

P5M4

 

 

0.477

 

 

 

 

 

 

 

P8M3

 

 

0.658

 

 

 

 

 

 

0.454

P8M4

 

 

 

 

 

 

 

 

 

-0.426

P8M5

-0.331

 

 

 

-0.327

 

 

 

 

 

P11M2

 

0.689

 

 

 

-0.419

 

 

 

 

P11M3

 

 

 

-0.402

 

-0.444

-0.452

 

 

 

P12M3

 

 

 

 

 

-0.445

 

 

-0.445

 

P13M1

 

 

 

 

 

 

 

-0.34

0.331

 

P13M6

-0.438

-0.532

 

0.322

 

 

 

 

-0.356

 

P16M1

 

 

 

 

 

 

 

 

 

0.412

P16M2

 

 

 

 

 

 

 

 

 

-0.523

P22M1

 

0.387

 

0.524

 

 

 

 

 

 

P22M6

 

 

0.308

 

 

 

 

 

 

 

P22M7

 

 

 

 

 

 

 

0/337

 

 

R2

0.533

0.588

0.591

0.356

0.404

0.335

0.438

0.424

0.553

0.449

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