Investigating the Effect of Cold Stress on the Physiological and Biochemical Traits of Leaves of Almond Cultivars
محورهای موضوعی : Almond
Mahrokh Rostami
1
,
Aliakbar Shokouhian
2
,
Asghar Estaji
3
,
Ali Asgharii
4
,
Ali Imani
5
,
Jalil Dejampour
6
1 - Ph.D. student in Physiology and Postharvest Physiology, University of Mohaghegh Ardabili, Ardabil, Iran
2 - Associate Professor, Department of Horticultural Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
3 - Associate Professor, Department of Horticultural Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
4 - The Professor of Plant Breeding, Department of Agronomy and Plant Breeding, University of Mohaghegh Ardabili, Ardabil, Iran
5 - Associate Professor, Temperate Fruit Research Center, Horticultural Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
6 - Associate Professor, Agricultural and Horticultural Research Department, Research and Training Center of the Province East Azerbaijan, Agricultural Research, Education and Extension Organization, Tabriz, Iran
کلید واژه: Almond, Antioxidant, Cold stress, Malondialdehyde, Peroxide,
چکیده مقاله :
The late frost spring is one of the most important limiting factors for almond production. However, different cultivars of almonds have various reactions to this process, the selection and introduction of cultivars that tolerate late frost spring is the most effective way to avoid cold damage. The present study aimed to investigate the importance of several physiological and biochemical characteristics related to cold adaptation mechanisms and their influence on tolerance to late-spring frost in almond cultivars. The experiment was arranged in a completely randomized design (six cultivars of almonds and cold stress +4°C) with three replications, was carried out in the Biotechnology Laboratory at Mohaghegh Ardabili University in 2022. Cold stress (+4°C) was applied on current-year branches of early-flowering (Seffid (A1), Mamaei clone (A2)), mid-flowering (SH21, A1-16) and late-flowering (MS13, AD55) almond tree cultivars for three days. The investigated traits included ion leakage, proline, proteins and soluble sugars, photosynthetic pigments, hydrogen peroxide, malondialdehyde and some antioxidant enzymes. The results by the selection index of ideal genotype (SIIG) showed that late-flowering cultivars MS13 and mid-flowering A1-16 were the most tolerant; the mid-flowering cultivar SH21 was semi-tolerant, whereas late-flowering cultivars AD55 and early-flowering A1 and A2 were the most susceptible to cold stress.
The late frost spring is one of the most important limiting factors for almond production. However, different cultivars of almonds have various reactions to this process, the selection and introduction of cultivars that tolerate late frost spring is the most effective way to avoid cold damage. The present study aimed to investigate the importance of several physiological and biochemical characteristics related to cold adaptation mechanisms and their influence on tolerance to late-spring frost in almond cultivars. The experiment was arranged in a completely randomized design (six cultivars of almonds and cold stress +4°C) with three replications, was carried out in the Biotechnology Laboratory at Mohaghegh Ardabili University in 2022. Cold stress (+4°C) was applied on current-year branches of early-flowering (Seffid (A1), Mamaei clone (A2)), mid-flowering (SH21, A1-16) and late-flowering (MS13, AD55) almond tree cultivars for three days. The investigated traits included ion leakage, proline, proteins and soluble sugars, photosynthetic pigments, hydrogen peroxide, malondialdehyde and some antioxidant enzymes. The results by the selection index of ideal genotype (SIIG) showed that late-flowering cultivars MS13 and mid-flowering A1-16 were the most tolerant; the mid-flowering cultivar SH21 was semi-tolerant, whereas late-flowering cultivars AD55 and early-flowering A1 and A2 were the most susceptible to cold stress.
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ORIGINAL ARTICLE
Investigating the Effect of Cold Stress on the Physiological and Biochemical Traits of Leaves of Almond Cultivars
Mahrokh Rostami1, Aliakbar Shokouhian2, Asghar Estaji*2, Ali Asghari 3, Ali Imani4, Jalil Dejampour 5
1Ph.D. student in Physiology and Postharvest Physiology, University of Mohaghegh Ardabili, Ardabil, Iran
2Associate Professor, Department of Horticultural Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
3The Professor of Plant Breeding, Department of Agronomy and Plant Breeding, University of Mohaghegh Ardabili, Ardabil, Iran
4Associate Professor, Temperate Fruit Research Center, Horticultural Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
5Associate Professor, Agricultural and Horticultural Research Department, Research and Training Center of the Province East Azerbaijan, Agricultural Research, Education and Extension Organization, Tabriz, Iran
K E Y W O R D S |
| A B S T R A C T |
Almond; Antioxidant; Cold stress; Malondialdehyde; Peroxide |
| The late frost spring is one of the most important limiting factors for almond production. However, different cultivars of almonds have various reactions to this process, the selection and introduction of cultivars that tolerate late frost spring is the most effective way to avoid cold damage. The present study aimed to investigate the importance of several physiological and biochemical characteristics related to cold adaptation mechanisms and their influence on tolerance to late-spring frost in almond cultivars. The experiment was arranged in a completely randomized design (six cultivars of almonds and cold stress +4°C) with three replications, was carried out in the Biotechnology Laboratory at Mohaghegh Ardabili University in 2022. Cold stress (+4°C) was applied on current-year branches of early-flowering (Seffid (A1), Mamaei clone (A2)), mid-flowering (SH21, A1-16) and late-flowering (MS13, AD55) almond tree cultivars for three days. The investigated traits included ion leakage, proline, proteins and soluble sugars, photosynthetic pigments, hydrogen peroxide, malondialdehyde and some antioxidant enzymes. The results by the selection index of ideal genotype (SIIG) showed that late-flowering cultivars MS13 and mid-flowering A1-16 were the most tolerant; the mid-flowering cultivar SH21 was semi-tolerant, whereas late-flowering cultivars AD55 and early-flowering A1 and A2 were the most susceptible to cold stress. |
Introduction
*Corresponding author: Email address: aestaji@yahoo.com Received: 13 February 2024; Received in revised form: 15 May 2024; Accepted: 26 November 2024 DOI: 10.60680/jon.2024.4021264
|
Materials and Methods
Plant materials and cold stress
The current research became arranged as a completely randomized design in three replications. The selected cultivars originated from the Karaj Horticultural Science Research Institute. They included early-flowering trees (Sefid (A1), Mamaei clone (A2)), mid-flowering (SH21, A1-16), and late-flowering (MS13, AD55) trees. At the early of May 2022 (at the stage of growth of young fruits), the current-year branches (30 cm long) were considered samples. Sampling involved taking two branch pieces from each tree (as a replication) and labeling them in a water bottle. The procedure was followed by transferring the samples to the Biotechnology Laboratory of Horticultural Sciences at Mohaghegh Ardabili University. Cold stress (+4°C) was applied to the samples for three days. Then, the leaf samples from each treatment were separated and packed in labeled aluminum foil and kept in a freezer at -80°C.
Determination of electrolyte leakage
To measure electrolyte leakage (EL), a method by Heidarpour et al. (2020) was used with a slight modification. A leaf was excised into pieces (one-centimeter diameter) from three mature fresh leaves of each sample. The samples were placed in a Falcon containing 10 ml of double-distilled water, which rotated at 110 rpm for 24 hours at room temperature on a shaker machine. Using a spectrophotometer (model SP-UV 200 Spectrum Instruments A. Perkin Elmer Company), the electrolytic conductivity was read at 280 nm (EC1). Then the samples were autoclaved at 121°C, with a pressure of 1.5 megapascals (MPa) for 20 min. After cooling, the electrolytic conductivity was read (EC2). The ion leakage (%) was measured according to equation 1:
EL %= EC1/EC2 × 100 (1)
Determination of proline
To measure proline, 0.1 gram of frozen leaf sample was ground in 2 ml of 3% sulfuric acid in a mortar. After centrifuging of the solution for 10 min at 4000 rpm, 1 ml of supernatant extract was mixed with 1 ml of ninhydrin reagent and one ml of glacial acid and placed in a hot water bath at 90°C for an hour. Two ml of toluene was added to the cooled reaction solution before vigorous stirring for 20 seconds. Standards of pure proline (0, 50, 100, and 200 μM) were prepared and the absorption values of supernatants and standards were read via a spectrophotometer (model SP-UV 200) at a wavelength of 520 nm. Then, the amount of proline was calculated according to equation 2 in terms of µmole of proline per gram of fresh weight (Bates et al., 1973).
µmole per gram tissue = [(µg proline ml-1) × ml toluene)/115.13µg µmole-1] / [(g sample) /5] (2)
Determination of soluble carbohydrate content
The soluble carbohydrate content was measured according to a method by Irigoyen et al. (1992). First, 0.16 grams of frozen leaf samples was ground with 1.6 mL of 95% ethanol in a porcelain mortar. The soluble part of the sample was separated using a sampler and poured into a falcon. The extraction process was repeated twice, each time using 1.6 ml of 75% ethanol. In the end, 100 ml of the alcoholic extract obtained from the centrifuge (3500 rpm for 10 min) were added to three ml of freshly prepared anthrone (150 mg of anthrone and 100 ml of sulfuric acid 72% (W/W)). The mixture was placed in a boiling water bath for 10 min to form a colored appearance. Then, the absorbance of each sample was read at 625 nm with a spectrophotometer (SP-UV 200). A standard curve (equation 3) was used for determining the amount of soluble carbohydrates in the samples. The result was expressed as mg per gram of leaf fresh weight.
Y= 0.0012x+ 0.1091 R2: 9927 (3)
Soluble sugars (mg g-1FW) = [X/1000 × V]/W (4)
X: Sugars of the extract
V: Sample volume of the extract
W: Wet sample weight
Determination of photosynthetic pigments
To measure photosynthetic pigments, 0.1 g of frozen leaf sample was ground in one ml of 80% ethanol at a low light intensity and cool temperature. After adding 4 ml of acetone to the samples, they were kept in a refrigerator at 4°C for 24 hours. Then, they were centrifuged at 3000 rpm for 10 min. The absorbance of supernatant solution was read at 663, 645, and 470 nm via a spectrophotometer (Model 6705 UV/Vis.). Using the following formulas (equation 5, 6, 7, 8), the chlorophyll content (a, b, total), and carotenoids were calculated as mg per gram of fresh weight (Arnon, 1967).
Chlorophyll a = [19.3(Absorption at 663nm) – 8.6(Absorption at 645nm)] ×V/ (1000×W) (5)
Chlorophyll b = [19.3(Absorption at 645nm) –3.6(Absorption at 663nm)] × V/ (1000×W) (6)
Total chlorophyll = Chlorophyll a + Chlorophyll b (7)
Carotenoids = [100 (Absorption at 470 nm) – 3.27 (mg chlorophyll a) – 104 (mg chlorophyll b)]/227 (8)
In this regard: V: Sample volume of the extract
W: Wet sample weight
Determination of hydrogen peroxide (H2O2)
The amount of hydrogen peroxide (H2O2) was measured according to the reaction of hydrogen peroxide with potassium iodide. For this purpose, 0.1 g of frozen leaf tissue was treated with 1.5 ml of 0.5% TCA. The resultant extract was centrifuged for 15 min at 5000 rpm. Then, 500 mL of the supernatant extract was added to 500 mL of 100 mM sodium phosphate buffer (pH = 7), followed by adding 2 mL of 1 M potassium iodide. The resultant mixture was placed in the dark at room temperature for one hour. Then, the absorbance of each sample was read at 390 nm. Finally, the concentration of hydrogen peroxide was calculated using a standard curve and expressed in µmol per gram of fresh weight (Alexieva et al., 2001).
Determination of membrane lipid peroxidation Malondialdehyde (MDA)
To begin, 0.12 g of leaf was homogenized in 1.5 mL of 0.1% trichloroacetic acid (TCA).Then; the solution was centrifuged for 5 min at 10,000 rpm. The supernatant was separated, and 2 ml of thiobarbituric acid 0.5% was added to the solution. The samples were incubated for 30 min in a bain-marie apparatus (95°C) and immediately kept in ice water to stop the reaction. After centrifugation at 10,000 g for 10 min, the absorbance of the supernatant was read at 532 and 600 nm using a spectrophotometer (model 6705 UV/Vis. Spectrophotometer JENWAY) (Davey et al., 2005). Membrane lipid peroxidation was calculated via the extinction coefficient which equaled 155 mM-1 cm-1, as nmol per gram of fresh weight, according to Equation 9.
MDA (nmol g-1 FW) = (A532 – A600) × V× 1000/155×W (9)
In this regard: V: Sample volume of the extract
W: Sample fresh weight in extraction
Extraction to measure total protein and enzymes
To extract and measure total soluble protein and enzymes, 2 ml of 100 mM sodium phosphate buffer (100 mM sodium phosphate with an acidity of 7.5, 2 mM EDTA and 0.7% PVP) were added to 0.1 g of powdered leaves in liquid nitrogen. After 30 Second vortexing, was centrifuged for 15 min at 14,000 rpm at 4°C. Supernatant extracts, after being transferred to 2 ml cold microtubes and centrifuged again (14000 rpm for 10 min at 4°C) were poured into microtubes (200 µL) using a sampler and and kept in a freezer at -80°C until the protein content and enzymes concentration were measured (Zhang and Shao, 2015).
Determination of total protein
To measure total protein, 100 µL of supernatant extract and 2900 µL of Bradford reagent were poured into a plastic cuvette. The solution was vortexed manually. The absorbance of each sample was measured in the dark after 2 min at 595 nm by a spectrophotometer (SP-UV 200) (Bradford, 1976). Finally, a standard curve was drawn. Equation 10 was used for measuring the amount of soluble protein. The results were expressed as mg per gram of leaf fresh weight.
Y= 0.0008x + 0.0407 R2= 0.972 (10)
Determination of Guaiacol peroxidase (GPX) enzyme activity
The activity of guaiacol peroxidase (GPX) enzyme was measured by using reaction medium containing 50 mM phosphate buffer (pH=7), 10 mM guaiacol, and 20 mM H2O2. The increase in absorbance at 470 nm wavelength was recorded from the start of the reaction for 3 min. The final result was expressed as an extinction coefficient of 26.6 mM-1 cm-1 in µmol/min/mg of protein (ZHANG and SHAO, 2015).
Determination of ascorbate peroxidase (APX) enzyme activity
The activity of the APX enzyme by the Nakano and Asada (1981) with some modifications, two reaction solutions were used, comprising 250 µM ascorbic acid and 0.5 mM EDTA; 1.5 mM H2O2 and 50 mM sodium phosphate buffer (pH=7).The decrease in absorbance of the reaction solution was read at 290 nm for 2 min. The final result was expressed as an extinction coefficient of 2.8 mM-1 cm-1 in µmol/min/mg of protein. Equation 11 was used for calculating the above enzymes.
Enzyme activity (µM Min-1. Mg-1 Pr) = [(∆Abs × Total assay volume) / (∆t × ε × l × Enzyme sample volume)] / (W × total protein) (11)
∆Abs: Change in absorbance
∆t: Time of incubation (min)
ε: Extinction coefficient of substrates in units of mM-1 cm-1
l: Cuvette diameter (1 cm)
W: Sample fresh weight in extraction
Calculation of ideal genotype selection index (SIIG)
To evaluate the cold tolerance of cultivars using all indices (plant properties) at the same time, the SIIG method was used. This index was calculated in several steps (Zali et al., 2015; Zali et al., 2016).
I - Creating the data matrix: based on the number of genotypes and the various factors analyzed, the data matrix is formed as follows:
In this matrix, xij is the value of the i-th genotype (i = 1, 2, ... n) concerning the j-th attribute (j = 1, 2, ... m).
II- Transforming the data matrix into a normal matrix: the following relationship is operated to normalize the data (without joining the data):
III- the Matrix R is defined as the following connection:
IV- Discovering the ideal genotype and the non-ideal (weak) genotype for each trait: at this stage, according to the type of trait and the researcher's viewpoint on each feature, the best genotype (ideal) and the weakest (non-ideal) was selected.
V- Estimating the distance from the ideal genotype (di+) and weak genotype (di-): for each genotype, the distance from ideal genotypes (di+) and weak (di-) was calculated using relationships 4 and 5.
In the above relationships, rij is the normalized value of the ith genotype (i = 1, 2, ..., n) concerning the jth index (j = 1, 2, ...m). Meanwhile, di + and di ¯ are the normalized values of ideal and weak genotypes for each jth index (j = 1, 2, …, m). Also, di+ is the distance from the ideal genotype and di- is the distance from the weak genotype.
VI- Calculation of the ideal genotype selection index (SIIG): In the last step, the ideal genotype selection index for each genotype is calculated from the following relationship:
Statistical analyses
Data analyses and the comparison of mean values were done by Duncan’s Multiple Range Test (P≤0.05) using SPSS 26 statistical software. Microsoft Excel was used for drawing the graphs, calculating the indices of cold tolerance, and finding the ideal genotype selection index.
Results
According to the results (Table 1), a statistically significant difference was observed among the cultivars regarding all traits, except the leaf protein content (P≤0.01), which showed the various responses of the cultivars to cold stress.
Table 1. Variance analysis of the effect of cold stress treatment on some physiological and biochemical traits of different almond cultivars.
Mean squares | df | S.O.V | |||||||||||
Chlorophyll total (mg g-1 FW) | Chlorophyll b (mg g-1 FW) | Chlorophyll a (mg g-1FW) | Soluble sugars (mg g FW-1) | Proline (µmol g-1 FW) | Electrolyte leakage (%) | ||||||||
0.555** | 0.023** | 0.391** | 126.30** | 1.175** | S.O.V | 5 | Cultivar | ||||||
0.077 | 0.003 | 0.065 | 5.19 | 0.065 | 11.361 | 12 | Error | ||||||
10.05 | 13.35 | 10.84 | 11.37 | 15.85 | 5.63 | C.V. (%) | |||||||
Mean squares | df | S.O.V | |||||||||||
APX (µmol min-1 mg-1 Pr) | GPX (µmol min-1 mg-1 Pr) | Proteins (mg g-1 FW) | MDA (nmol g-1 FW) | H2O2 (µmol g-1 FW) | Carotenoids (mg g-1 FW) | ||||||||
0.097** | 0.446** | 0.233ns | 36.981** | 0.043** | 0.125** | 5 | Cultivar | ||||||
0.006 | 0.000 | 0.081 | 6.763 | 0.008 | 0.007 | 12 | Error | ||||||
20.11 | 0 | 15.44 | 8.56 | 28.75 | 20.06 | C.V. (%) | |||||||
The comparison of mean values (Fig. 1-A) demonstrated that the percentage of electrolyte leakage differed between the studied almond cultivars. Cultivar A1 (91.01%) had the maximum percentage, and cultivar MS13 (10.94%) had the minimum percentage of ion leakage. However, no statistically significant difference was observed between A1 and AD55 cultivars. Cultivars A2, SH21, and A1-16 were categorized in the second and fourth diagrams, respectively.
The comparison of mean values (Fig. 1-B) showed that all cultivars reacted to cold stress by generating different amounts of proline. Depending on the cultivar, the stress response was different. MS13 had the highest leaf proline content (2.54 µmol g-1 FW). No statistically significant difference was observed in the proline level among the A2, A1-16, and SH21 cultivars. The lowest amount of proline was in AD55 (0.905 µmolg-1 FW), which was not significantly different from A1 (0.962 µmol g-1 FW).
According to the comparison of mean values (Fig. 1-C), MS13 (31.5 mg g-1 FW) and A2 (13.76 mg g-1 FW) had the highest and lowest soluble sugar content, respectively. Cultivars SH21, A1-16, and AD55 were almost similar in their soluble sugar content and did not show statistically significant differences.
As shown (Fig. 1-D, E, F), leaf chlorophyll pigments responded differently to the low-temperature stress, as revealed by measuring the chlorophyll a and total chlorophyll content. Cultivar A1 had the highest chlorophyll content with a significant difference compared to the rest of the cultivars. Therefore, the maximum amounts of chlorophyll a and total chlorophyll were observed in cultivar A1 (3.03 mg g-1 FW and 3.56 mg g-1 FW, respectively) and their minimum values were observed in cultivar MS13 (1.94 mg g-1 FW and 2.25 mg g-1 FW, respectively). Regarding chlorophyll b, the highest concentration was observed in cultivar A1 (0.531 mg g-1 FW), which had no statistically significant difference with cultivars A1-16 (0.454 mg g-1 FW) and AD55 (0.437 mg g-1 FW). The lowest chlorophyll b concentration was observed in SH21 (0.304 mg g-1 FW), which did not show a statistically significant difference with MS13 (0.312 mg g-1FW).
The comparison of mean values (Fig. 1-G) showed that the cold stress (+4 °C) significantly affected the carotenoid content in each cultivar. The highest carotenoid content was recorded in cultivar SH21 (0.735 mg g-1 FW). Meanwhile, A2 had the lowest carotenoid content (0.27 mg g-1 FW) but did not show statistically significant differences with AD55, A1-16, and A1 cultivars. Also, the carotenoid content in MS13 (0.613 mg g-1 FW) ranked second among the other cultivars.
The comparison of mean values (Fig. 1-H) showed that the highest amount of H2O2 (0.45 µmol g-1 FW) was recorded in cultivar SH21, whereas the lowest (0.144 µmol g-1 FW) occurred in cultivar A1. The amount of H2O2 in the SH21 cultivar was not significantly different from the AD55, MS13 and A1-16 cultivars.
Lipid peroxidation can be estimated quantitatively by measuring the malondialdehyde concentration. Accordingly, the highest concentration of malondialdehyde (34.68 nmol g-1 FW) was observed in the MS13 cultivar (Fig. 1-I). However, this concentration was not significantly different from that observed in A1 and AD55 cultivars. The lowest malondialdehyde concentration (25.65 nmol g-1 FW) was recorded in cultivar A1-16. These results are not consistent with previous reports by Aazami et al. (2021), Neisi et al. (2022), and Zhang et al. (2022) who reported that cold-tolerant cultivars show less lipid peroxidation when faced with cold stress.
The A1-16 cultivar had the highest GPX enzyme activity (0.987 µmol min-1 mg-1 Pr) compared to the other cultivars (Fig. 1-J). The lowest GPX activity (0.004 µmol min-1 mg-1 Pr) was observed in the A1 cultivar. The peroxidase enzyme (POX) is usually tasked with collecting reactive oxygen species to prevent damage on the plasma membrane (Hashempour et al., 2014) and ultimately increases tolerance to stress in plants.
The comparison of mean values (Fig. 1-K) in this experiment showed that the APX content was highest in cultivar MS13 (0.599 µmol min-1 mg-1 Pr), which was not significantly different from the APX content of A1-16 and SH21 cultivars. Also, the lowest concentration of this antioxidant (0.186 µmol min-1 mg-1 Pr) was observed in cultivar A2, but showed no significant difference with cultivars AD55 and A1.
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Fig. 1. Effect of cold stress +4 °C on electrolyte leakage (A), Proline (B), soluble sugars (C), chlorophyll a (D), b (E) and total (F), carotenoids (G), H2O2 (H), MDA (I), GPX (J) and APX (K) leaves of almond cultivars. Different Alphabets indicate significant differences were observed by using Duncan's multi-range test at 5% probability level. | |
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Fig. 1. Continued. | |
Discussion
Electrolyte leakage occurs in cells when the membrane is damaged due to stress (Afshari et al., 2014). Previous studies showed that the plasma membrane of cold-tolerant plants is less vulnerable to damage, compared to susceptible plants (Nazari et al., 2012). Accordingly, almond cultivars are among plants with electrolyte leakage values that increase in response to cold stress (Mousavi et al., 2014). Also, Imani et al. (2011) examined 60 cultivars of almonds, indicating that the severity of frost damage depends on the genotype of the plant, and more resistant genotypes show less ion leakage. Therefore, this trait can be used as an indicator to identify tolerant and susceptible cultivars. In the current research, MS13 showed the least amount of electrolyte leakage and probably had the smallest extent of damage and maximum tolerance to cold stress.
In cold-tolerant plants, proline was the main amino acid, which acted as a protective component against the cold. A high proline concentration in tissues serves as a mechanism for adaptation to cold stress (Aazami et al., 2021). The late-flowering cultivar AD55 had the lowest proline content, which shows that the proline content does not depend on the flowering time of the cultivars.
Total soluble sugars have a role in the osmotic regulation of cells during environmental stress, such as drought and low temperatures. Some plants tolerate low-temperature stress by accumulating large amounts of osmotic protective soluble substances, such as soluble sugars. According to previous research, osmotic protectors such as sugar and proline significantly increase the tolerance of plants to cold stress (Ben Ahmed et al., 2011; Zhang et al. 2012 ; Brown et al., 2019), which is consistent with the results of this research.
Accordingly, since chlorophyll and proline are both made of a common precursor (glutamate), an increase in proline content at times of stress may decrease the chlorophyll concentration (Khalid et al., 2010). The higher chlorophyll concentration in cultivar A1 was probably due to stress conditions of intracellular water loss, which caused the chlorophyll concentration to increase per unit area (Teulate et al., 1997). Also, it could be due to the characteristics of the cultivar that had more chlorophyll content than the rest of the cultivars.
A high level of carotenoids may occur during cold stress and have a protective role (Amini, 2019). Cold resistance sometimes increases pigment concentration, such as carotenoids, which are oxidized at low temperatures. Their oxidization acts as a shield against the loss of chlorophyll a and b, thereby preventing frost damage (Nasiriyan Jazi, 2020).
The accumulation of H2O2 as a low-risk ROS, compared to highly toxic oxygen radicals such as superoxide, is tasked with signaling but can be eliminated in later stages by other components in the defense system, including antioxidant enzymes (APX, CAT, SOD, and GPX) (Amini,2019). Although excess ROS are toxic to plant cells, a certain level of their production is required for a successful response to stress. ROS has an important signaling role in plants and affects the expression of specific genes, thereby controlling many processes such as abiotic stress responses (Ahmad et al., 2018).
Mano et al. (2010) reported that several types of aldehydes are present in the leaves and roots of plants at micromolar levels under non-stress conditions. However, when exposed to abiotic stress, plants show an increased amount of these toxic substances. Accordingly, it can be assumed that the initial malondialdehyde concentration in the MS13 cultivar was high. But to make a more accurate assessment of cultivar reactions to lipid membrane damage, it is necessary to take more samples during the stress period and show precisely the changes in Cold concentration. Also, it may be possible that such results were specific to the anatomical, physiological and biochemical characteristics of the cultivars when studied against cold stress.
Zrig et al. (2015) reported that the increase in APX enzyme activity in the ‘Mazzetto’ almond cultivar had a protective role against abiotic stress, which is consistent with the current findings. The main ROS (reactive oxygen species) enzymes include SOD, APX, CAT and GPX. The balance between the activities of these enzymes in cells is crucial for maintaining a stable level of ROS (Nasiriyan Jazi, 2020). Under normal conditions, plant cells have a stable level of ROS. However, various stress conditions, including cold stress, lead to excessive production of ROS. Nonetheless, excessive levels of ROS are removed by increasing the activity of different antioxidant enzymes such as SOD, APX, CAT and GPX (Jan et al., 2018).
In the current research, the APX enzyme played a more significant role against cold stress compared to the GPX enzyme. Its activity was more timely and prominent than that of GPX. The cold-tolerant cultivar had higher APX activity, whereas the susceptible cultivars showed lower levels of both enzymes (GPX and APX), indicating severe damage to the tissues of these cultivars. According to Nourredine et al. (2015), GPX activity increased slowly in the first 5 days after applying the stress but then showed a significant increase in stress-tolerant samples in the subsequent 8 to 11 days. Also, Ensafi (2019) indicated that the weak activity of this enzyme in the shoot samples of grape cultivars can be attributed to its weak activity in the initial days of exposure to stress, compared to other antioxidant enzymes, thereby confirming our research. Amini (2019) studied cold stress on chickpea cultivars and reported that antioxidant enzymes are more active in tolerant cultivars, compared to susceptible cultivars. This stronger activity mitigated the severity of damage caused by H2O2 and EL when the plants were exposed to cold stress.
Cultivar MS13 was generally more adaptable to cold stress (+4°C), whereas the AD55, A1, and A2 cultivars were most susceptible.
To select stress-tolerant cultivars, paying attention to one index alone may not lead to favorable results, whereas relying on several indices can increase the chances of identifying ideal genotypes. Accordingly, the SIIG technique for selecting ideal genotypes has been used for the first time to integrate different indices of drought tolerance and increase the efficiency of selecting ideal genotypes. With the help of the SIIG technique, all indices and traits become a single index, making the classification of genotypes much easier. In other words, negative and positive indicators can be used in a combined form in this technique. The best genotype is the closest genotype to the ideal genotype and the farthest from the non-ideal genotype (Zali et al., 2015; Zali et al., 2016). Therefore, an effective evaluation of cold tolerance in cultivars can be realized by using all available indices simultaneously. In this research, the SIIG index was calculated while considering 12 traits. The cultivars with the highest amounts of proline, soluble sugars, photosynthetic pigments, protein, and antioxidant enzymes, but the lowest amount of ion leakage, hydrogen peroxide, and malondialdehyde were considered ideal. The value of SIIG changed between zero and one. The closer it was to one, the more ideal the genotype was. According to this index, the late-flowering cultivar MS13 (0.899) and mid-flowering A1-16 (0.67) were the most tolerant. The mid-flowering cultivar SH21 (0.44) was semi-tolerant. Late-flowering cultivars AD55 (0.06) and early-flowering A1 (0.106) and A2 (0.164) were the most susceptible to frost (Table 2).
Table 2. The selection index of ideal genotype (SIIG) based on different indices of cold tolerance and distances from ideal and weak genotypes. | ||||
Cold tolerance | SIIG index | d- | d+ | Cultivars |
HT | 0.899 | 0.4217 | 0.0472 | MS13 |
HT | 0.670 | 0.2912 | 0.1427 | A1-16 |
MT | 0.440 | 0.1878 | 0.2389 | SH21 |
LT | 0.164 | 0.0706 | 0.3590 | A2 |
LT | 0.106 | 0.0491 | 0.4160 | A1 |
LT | 0.060 | 0.0279 | 0.4035 | AD55 |
Note: HT, high tolerance; MT, medium tolerance; LT, low tolerance. | ||||
Conclusions
Physiological and biochemical traits were measured in six almond cultivars. Since each cultivar had a different flowering time, each responded uniquely to low-temperature stress (+4°C). The cultivars had different physicochemical reactions to cold stress. The SIIG index revealed that the AD55 cultivar (late-flowering), A1 and A2 (early-flowering) were the most susceptible. The SH21 was semi-tolerant. MS13 and A1-16 were the most cold-tolerant. Thus, cold tolerance does not correlate with the date of flowering. Ultimately, the late-flowering cultivar AD55 was considered the most susceptible.
Acknowledgments
Plant materials in this research were provided by the Karaj Horticultural Science Research Station located in Meshkindasht. The authors are grateful for their assistance. We also thank the support of the Faculty of Agriculture and Natural Resources of Mohaghegh Ardabili University for providing financial and laboratory facilities.
Conflict of interests
The authors declare that they have no conflict of interest.
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