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Research Article

Legume Research, volume 44 issue 12 (december 2021) : 1419-1429

Evaluation of Soybean (Glycine max L.) Genotypes on the Basis of Biochemical Contents and Anti-oxidant Enzyme Activities

Akash Sharma1, M.K. Tripathi1,*, Sushma Tiwari1, Neha Gupta1, Niraj Tripathi2, Nishi Mishra1
1Department of Plant Molecular Biology and Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Agricultural University, Gwalior-474 002, Madhya Pradesh, India.
2Directorate of Research Services, Jawaharlal Nehru Agricultural University, Jabalpur-482 004, Madhya Pradesh, India.

  • Submitted29-05-2021|

  • Accepted24-07-2021|

  • First Online 07-08-2021|

  • doi 10.18805/LR-4678

Cite article:- Sharma Akash, Tripathi M.K., Tiwari Sushma, Gupta Neha, Tripathi Niraj, Mishra Nishi (2021). Evaluation of Soybean (Glycine max L.) Genotypes on the Basis of Biochemical Contents and Anti-oxidant Enzyme Activities . Legume Research. 44(12): 1419-1429. doi: 10.18805/LR-4678.

ABSTRACT

Background: Soybean is an important leguminous crop. Abnormal weather has played an enormous role in the strident decline in crop yields. Drought is considered as a significant abiotic factor responsible for yield reduction in soybean.

Methods: The present work was carried out in order to screen soybean genotypes for their drought tolerance ability by means of different biochemical and antioxidant enzymatic activities responses.

Conclusion: On the basis of biochemical parameters and anti-oxidant enzymatic activities, soybean genotype viz., RVS-211-77, RVS-211-75, NRC-7, SL-96, NRC-136, AMS100-39, SL-96, RVS-2012-01, RVS-211-73 and JS97-52 have been identified with better performance and can be used as parents for further crop improvement programme to breed drought tolerant variety.

INTRODUCTION

Soybean [Glycine max (L.) Merril] is a vital and inexpensive protein and oil seed crop. Therefore, in numerous emerging nations, it is used as an imperative constituent of human foods and animal feed stuff. Its seed comprehend near 20-22% edible oil, 32-35% protein and 35-38% carbohydrates (17% of that is alimental fiber) and around 5% ash in addition to minerals and vitamins (Tripathi and Tiwari, 2004; Tiwari and Tripathi, 2005; Mishra et al., 2020; Upadhyay et al., 2020a; Mishra et al., 2021a). Soybean oil plays a vital part in nourishment of human as it has better extent of indispensable unsaturated fatty acids like omega-3, omega-6  and omega-9.
       
Plant growth and yield of soybean are decidedly diminished by numerous biotic and abiotic factors. Drought is prime environmental stress conditions that diminutions crop production and excellence, therefore posturing a thoughtful hazard to crop production (Mishra et al., 2021b; Upadhyay et al., 2020b). Stress constrains the synthesis of photosynthetic harvests due to reductions in leaf photosynthetic dimensions (Anjum et al., 2011) and the acceleration of leaf wilting and ageing (Pinheiro et al., 2005).
       
Drought influences plant growth through oxidative stress tracked by illnesses in diverse biological progressions. Responsive oxygen species (ROS) distresses antioxidant enzyme bustle and waning photosynthetic action and stomatal opening. Screening of drought tolerant genotypes on the basis of different biochemical parameters and anti-enzymatic activities may deliver a sordid for the development of new plant varieties applying conventional along with molecular breeding tactics to fight drought. The current investigation was executed to monitor drought tolerant soybean genotype (s) based on manifestation of different biochemical parameters and antioxidant enzymes activities.

MATERIALS AND METHODS

The present study was consisted of 60 soybean genotypes (Table 1) with conflicting reactions to drought viz., susceptibility and tolerance. The field trial was deportment at the investigational field, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, India during July 2019 to May 2020 in randomized block design in three rows with two replications and row to row distance was kept 30 cm. Data were recorded after 70 days of sowing from five random selected plants of each line and replication for diverse biochemical parameters. The data were investigated according to method proposed by Snedecor and Cochran (1967).
 

Table 1: List of soybean genotypes with their source.


 
Biochemical parameters
 
Photosynthetic pigments
 
Photosynthetic pigments were estimated by adopting method of Arnon (1949). Measurement of photosynthetic pigment was accomplished employing UV-VIS spectrophoto- -meter for documenting absorbance at 470, 645 and 663 nm. The magnitude of chlorophyll a, chlorophyll b and total chlorophyll was computed by following formulae:
 
Chla = (12.7 × Abs663) - (2.6 x Abs645) × 10 ml of acetone/100 mg leaf tissue.
Chlb = (22.9 Abs645) - (4.68 Abs663) × 10 ml of acetone/100 mg leaf tissue.
Chla+b =  (22.9 Abs645) - (4.68 Abs663) × 10 ml of acetone/100 mg leaf tissue.
 
Estimation of sugar (mg g-1 fresh weight) and proline (unit) contents
 
Sugar quantity was computed by mentioning glucose standard curve as adopted by Kachare (2017) in soybean. Free proline content in leaves was determined according to the method recommended by Bates et al., (1973).
 
Membrane stability index
 
Membrane stability index (MSI) was computed by using following formula.
 
 
           Membrane Stability Index = [1 - {C1/C2}] × 100
 
 
C1 = Electrical conductivity of water containing the leaf sample in set one.
C2 = Electrical conductivity of water containing the leaf sample  in set two.
 
Antioxidant enzyme activity (catalase, glutathione reductase and peroxidase)
 
The actions of diverse antioxidant enzymes have been determined employing spectrophotometer. Estimation of Ascorbate peroxidase (APX) activity was done following the method of Nakano and Asada (1981). In the first step diluted enzyme extract (20 μl) was added in 50 mM potassium phosphate buffer (880 μl) containing 0.5 mM ascorbate. In the second step 1 mM H2O2 (100 μl) was added to start the reaction. Further the absorbance was recorded at 15 s interval for 2 min at 290 nm. Catalase activity was estimated by the UV method of Aebi (1983) with some modifications. 100 μl of diluted enzyme isolated from leaves was mixed with 800 μl of 50 mM potassium phosphate buffer for the experiment (pH 7.0). 100 μl of 100 mM hydrogen peroxide was used to start the reaction. The change in absorbance was measured at 240 nm at 15second intervals. GR activity was measured according to the method of Smith et al., (1988) with needed modifications. Diluted enzyme extract (25 μl), 5, 5’-dithio-bis (2-nitrobenzoic acid) (250 μl), 1mM EDTA (20 μl) and 0.2 mM oxidized glutathione (100 μl) were added in 175 μl of 50 mM potassium phosphate buffer (pH 7.6). In the next step 50 μl of 5 mM NADPH added to the reaction mixture to initiate GR activity. Changes in absorbance at 412 nm were recorded at 15 s interval. Guaiacol peroxidase (POX) activities were analyzed the method adopted by Kachare (2017).

RESULTS AND DISCUSSION

Biochemical analysis
 
The analysis of variance presented in Table 2 clearly indicated existence of substantial amount of variations among 60 soybean genotypes included in the present study for all biochemical parameters investigated. Chlorophyll is the core photosynthetic pigments that decide biomass of plants. Generally, the magnitude of chlorophyll content in leaves regulates the rate of photosynthesis. Drought stress constrains the photosynthesis by means of instigating alterations in chlorophyll content. Total Chlorophyll content in mg/ml varied significantly in range of 36.58-58.33 mg/ml in present investigation, maximum with genotype RVS 2011-75 while the minimum was in JS20-53*JS20-34. Anjum et al., (2011) displayed a decline in the quantity of chlorophyll because of forfeiture of chloroplast membranes in drought susceptible genotypes. Analogous reduction in chlorophyll levels in many other plant species as well as soybean (Zhang et al., 2007; Mishra et al., 2021c), chickpea (Sahu et al., 2020) and peal millet (Choudhary et al., 2021).
 

Table 2: Mean performance of different biochemical parameters and antioxidant enzymes activities of soybean genotypes.


       
Soluble sugars are the key osmotic modification constituents and therefore are significant pointers of tolerance/resistance in genotypes. Total sugar in mg/g varied significantly in range of 2.4-5.9 mg/g with maximum in genotype RVS2011-77 trailed by genotypes RVS2011-73 and RVS 2011-10, while the minimum was perceived in genotype CAT87 and JS335 tracked by genotype PK885. Genotypes pursuing higher sugar content might be drought tolerant as reported earlier by Kachare et al., (2019) and Mishra et al., (2021c). The augmentations in MSI designate the lessening of lipid peroxidation with oxidative bursts under water stress conditions. Genotype PS1611 exhibited maximum membrane stability (MI) tracked by genotypes viz., JS20-90 and JSM240*SL517 whereas, it was displayed by genotype AMS-MB 5-18 intimately pursued by genotypes Hardee and Gaurav.
       
Proline is trusted as an authoritative drought tolerance pointer. Proline content in μg/g varied meaningly between 65.31-107.27ìg/g with utmost in genotype EC 602288 (107.27) followed by genotypes JS97-52 (105.45), EC538828 (101.88) and NRC7 (100.25), more than 100μg/g. However, the minimum was observed in genotype JS2009*PS1475 (65.31) trailed by genotypes JS2063*JS95-60 (67.63) and SL 525 (67.9). Genotypes exhibiting higher proline content may have drought tolerance as suggested by Kachare et al., (2019) and Mishra et al., (2021c) as they also concluded during their study on screening of soybean genotypes. It may be perhaps due to increased proline content maintains cell water level under drought (Choudhary et al., 2021).
 
Cluster analysis of different biochemical parameters (Total chlorophyll content, proline content, total sugar and MSI)
 
On the basis of dendrogram (Fig 1), genotypes formed two clusters. Major cluster consisted 53 genotypes while minor cluster had only 7 genotypes including NRC7, EC602288, JS97-52, EC538828, Bragg, PK472 and Hardee. Major group further divided into two groups. Major sub group consisted 51 genotypes, however minor sub group had two genotypes, namely JS 20-90 and PS 1611. Major sub group further divided into two groups. Major group consisted 41 genotypes, however minor group had 10 genotypes including SL688, SL525, JS2063*JS95-60, PK885, AMS-MB 5-18, SL983, C-2797, Gaurav, HIMSO-1685 and Shivalika. Major group consisted 41 genotypes viz., RVS2011-77, RVS2011-73, BAUS102, RVS2011-21,  SL1074, JSM240*SL517, SL96, SP-37, JS335, CAT87, AUKS-174, RVS2011-75, JS20-78, RVS2011-32, NEC 37, RVS2011-04, NRC136, PS1225, AMS100-39, AMS243, AMS475, JS93-05, RVS2001-04, RVS28, Young, JS21-17, RVS2011-76, RVS2011-10, DS3105, JS2009* PS1475, RVS2012-15, SL958, EC46728, RVS 2012-01, SL 995, RSC 1052, RVS 2011-35, SL 953, DS 3106, RVS 2011-74 and JS20-53*JS 20-34.
 

Fig 1: Dendrogram showing relationship among genotypes based on different biochemical contents (Total chlorophyll, proline, total sugar and MSI).


 
Biochemical analysis based hierarchical cluster analysis
 
Bestowing to the hierarchical cluster investigation and the gratified values (Table 2), the active countenance outline was governed and is displayed in Fig 1. Multivariate examination owing to diversity was executed through the UPGMA. The average worth of biochemical parameters the dendrogram was discriminated into two major clusters i.e., I and II. Cluster I and II divided into subclusters. These clusters further subdivided into minor clusters. Genotypes JS-20-78, AMS-100-99, PK-472 and Hardee fallen in a seprate group.
 
Biochemical parameters based principal component analysis (PCA)
 
Principal component analysis was conducted by taking biochemical variables concurrently. The PCA correlation illustrated that which variety possessed higher and lower content occupying unique position towards the graph (Fig 2). In these four variables, total sugar content had highest variability (58.8%).
 

Fig 2: Clustering pattern of different genotypes for different biochemical contents.


 
Antioxidant enzymes activities
 
Antioxidative enzymes perform a key part in defending plants from the damaging consequences of drought stress. Plants change enzymatic devices encompassed in the enzymatic rummaging of Reactive Oxygen Species (ROS) for instance APX, CAT, GR and PDX. Lessons on soybean disclosed that stress tolerant plants are usually bestowed with competent antioxidant protection arrangement (Vasconcelos et al., 2009). Earlier it was also noticed that CAT and GR had a higher ROS hunting action than APX, so, it could be assumed that CAT and GR are the most significant ROS foraging enzymes in soybean owing to their involvement in tolerance machinery. This may be entire capacity of a particular genotype to combat against these stresses.
       
Among antioxidant enzymes, Ascorbate peroxidase delivers protection machinery in plants to convalesce from drought. APX in unit/mg protein/min varied between 0.22 to 2.08 with maximum in genotype SL-96 (2.08) and minimum in EC-602288 (0.22). Genotypes having enhanced APX bustle may be drought tolerant probably because of advent of protuberant H2O2 detoxification with anticipation of H2O2-mediated cell injury. Elevated APX action in soybean under drought stress has been previously documented (Kachare et al., 2019). In this experiment, CAT action in unit/mg protein/min varied in range of 0.31-1.21. CAT activities increased in the genotypes viz., RVS2011-73 tracked by SL 96. GR commotion in unit/mg protein/min varied in range of 0.21-0.91 with maximum in genotype NRC7. However, the minimum was observed in genotypes SL 95. Kachare et al., (2019) also observed a significant elevation in GR activity in some soybean genotypes. Guaiacol peroxidase activity in unit/mg protein/min ranged between 0.22-2. During the current study, up surged PDX motion was recorded in genotypes i.e., SL 96, AMS100-39, RVS 2012-01 and NRC7. Similar to the present research, Kachare (2017) and Mishra et al., (2021c) noticed directly proportional relation between PDX activity and level of water stress in soybean.
 
Cluster analysis of pooled antioxidative enzymes data
 
In antioxidant enzymes data based dendrogram the genotypes were divided into two clusters i.e., major cluster and minor cluster. The major cluster contained 58 genotypes while the minor cluster had only 2 genotypes viz., RVS 2012-01 and SL 96. Major group further subdivided into two groups major sub group 1 and minor sub group 2. Major sub group 1 consisted 51 genotypes, however minor sub group had only 2 genotypes, namely NRC-7 and AMS 100-39. Minor sub group further subdivided into two parts. RVS-2011-73 genotypes came in outgroup of the cluster.
 
Antioxidant activities estimate based hierarchical cluster analysis
 
Dynamic expression profile was constructed on the basis of hierarchical cluster analysis and the content values presented in Fig 3. Multivariate analysis owing to diversity was executed using the UPGMA. The mean value of antioxidant enzymes activities of different genotypes depicting in each cluster was presented in the generated dendrogram and distinguished into two major clusters (I and II). Cluster I and II divided into subclusters. These clusters further subdivided into minor clusters (Fig 4 and Fig 5). Cluster I consisted NRC-136 and AMS100-39 genotypes as an outer group and cluster II also consisted two genotypes viz., RVS 2012-01 and SL-96 as an outgroup.
 

Fig 3: Principal component analysis of biochemical contents.


 

Fig 4: Dendrogram showing relationship among genotypes based on different anti-oxidative enzymes activities (Ascorbate peroxidase, catalase, glutathione reductase and guaiacol peroxidase).


 

Fig 5: Clustering pattern of soybean genotypes for antioxidant enzymatic activities.


 
Antioxidant activities based principal component analysis (PCA)
 
Principal component analysis (PCA) was drawn by considering biochemical variables instantaneously. The decoration of variations exemplified by the PCA designated by correlation coefficients explained for pair-wise connotation of the traits. The PCA correlation illustrated that genotype possessed higher and lower antioxidative content occupying unique position towards the graph (Fig 6). In these four variables, Glutathione reductase content had highest variability (64.7%).
 

Fig 6: Principal Component analysis of antioxidant enzymatic activities of genotypes.

CONCLUSION

In this study, sixty soybean genotypes were evaluated on the basis of biochemical as well as antioxidant enzymes under drought conditions. The findings suggest that the genotypes RVS-2011-77, RVS-2011-75, NRC-7, SL-96, NRC-136, AMS100-39, SL-96, RVS 2012-01, RVS-2011-73 and JS97-52 can be used for further crop improvement programme.

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