Interactive effects of binary combinations of manganese with other heavy metals on metal uptake and antioxidative enzymes in Brassica juncea L. seedlings

Abstract

The present study investigates the interactive mechanism involved during the uptake of heavy metals and stress tolerance in Brassica juncea L. seedlings under the influence of Mn(II) in binary combinations with Cr(VI), Ni(II), Co(II) and Cu(II) in terms of changes in antioxidative enzymes-superoxide dismutase (SOD), guaiacol peroxidase (GPX), catalase (CAT), ascorbate peroxidase (APX) and glutathione reductase (GR). The order of uptake of heavy metals by the seedlings in single metal solutions was observed to be, Mn>Cu>Co>Cr>Ni. As revealed by multiple regression and path analysis, manganese (Mn) in binary combination with other heavy metals mutually decreased the uptake of each other. Mn and other metals were antagonistic to each other for the activities of SOD, GPX, APX and GR, and decreased the effect of each other through interaction, whereas antagonism between these metals increased the activity of CAT.

Keywords:

• metal interactions
• chromium
• nickel
• superoxide dismutase
• guaiacol peroxidase
• catalase
• ascorbate peroxidase
• glutathione reductase

Abbreviations

APX	=	ascorbate peroxidase
CAT	=	catalase
DHAR	=	dehydroascorbatereductase
GR	=	glutathione reductase
MDHAR	=	monodehydroascorbatereductase
ROS	=	reactive oxygen species
SOD	=	superoxide dismutase

Introduction

Phytoremediation is emerging as a cost-effective, ecofriendly ‘green clean’ technology for the abatement of heavy metal pollution at hazardous waste sites (Ebbs and Kochian 1998; Lai and Chen 2009). However, the understanding of the physiological responses of phytoremediators to the heavy metals is a prerequisite in the restoration of heavy metal polluted sites (Schnoor 2000; Chaney et al. 2007). The tolerance potential of plants to heavy metals depends upon various physiological and molecular mechanisms (Khan et al. 2009). A common feature of heavy metal stress is the increased production of reactive oxygen species (ROS) such as superoxide anion radical (·O²⁻), hydrogen peroxide (H₂O₂), hydroxyl radical (·OH), singlet oxygen (¹O₂) etc., in plant tissues (DiToppi and Gabbrielli 1999; Arora et al. 2002). ROS are partially reduced forms of atmospheric oxygen which are also generated in plant cells during normal metabolic processes (Fridovich 1986; Alscher 1989; Dat et al. 1998). Heavy metal stress enhances the production of ROS up to 30-fold, leading to oxidative stress (Mittler 2002). These species react with lipids, proteins, pigments and nucleic acids and cause lipid peroxidation, membrane damage and inactivation of enzymes, thus affecting the cell viability (Halliwell 1994; Blokhina et al. 2002). In response to heavy metal stress, plants employ various defense mechanisms which result in avoidance, exclusion and/or detoxification of heavy metal ions (Hall 2002).

The synchronous action of various antioxidative enzymes such as CAT, SOD, APX and the thiol-regulated enzymes (DHAR, MDHAR and GR) of the ascorbate-glutathione pathway is a predominant mechanism of ROS quenching (Paridha et al. 2004; Shanker et al. 2004). Low molecular weight antioxidant metabolites such as ascorbic acid and reduced glutathione and organic ligands rich in cysteine and non-protein thiols also provide protection against ROS (Cobbett 2000; Chen et al. 2001). By evoking antioxidative enzyme induction as a general adaptive response, hyperaccumulator plants used for phytoremediation have adapted themselves well against oxidative stress caused by heavy metals (Van and Clijsters 1999; Narang et al. 2008; Ansari et al. 2009). Several studies have been carried out on the defense mechanism of plants under oxidative stress (Mizuno et al. 1988; Hegedüs et al. 2001; Lee et al. 2001; Cao et al. 2004; Wang et al. 2004a,b). The coexisting heavy metals at multi-elemental contaminated sites have synergistic, additive or antagonistic effects in the plants (Rosko and Rachlin 1977; Martin-Prevel et al. 1987; Symeonidis and Karataglis 1992 ; Siedlecka 1995; Krupa et al. 2002). The knowledge of detoxification mechanism is of critical importance from a practical standpoint of optimizing the mechanism of phytoremediation through improvement in cellular defense mechanism to combat heavy metal stress in hyperaccumulator plants (Aravind and Prasad 2005). In view of this, the present study was undertaken to investigate the interactive effects of manganese (Mn) in combination with other heavy metals on the growth, metal uptake and antioxidative mechanism of Brassica juncea.

Material and methods

Heavy metal treatments

Certified seeds of B. juncea L. cv ‘PBR-91’ were surface sterilized with 0.01% HgCl_2. For the study of heavy metal uptake, the concentrations of heavy metals used were: 0, 25, 50 and 100 mg l⁻¹. The seedlings did not survive in solutions containing chromium (Cr), nickel (Ni), cobalt (Co) and copper (Cu) at concentrations higher than 100 mg l⁻¹.

Therefore, for the study on antioxidative enzymes, the following treatments were selected:

1. Single metal treatments–0 and 100 mg l⁻¹ of each metal (Cr, Mn, Ni, Co and Cu).

2. Binary treatments–Mn treatments in binary combinations with other metals, all at 100 mg l⁻¹.

Seeds were germinated on Whatman No. 1 filter paper, lined inside 9 cm diameter sterilized Petri plates moistened with aqueous solutions of heavy metals in single and binary combinations, pH 6.5. Solutions were prepared using analytical reagent (AR) grade chemicals, K₂CrO₄, NiSO₄·6H₂O, CoCl₂·6H₂O, CuSO₄·5H₂O and ZnSO₄·7H₂O. Chemicals used in the study were purchased from Sigma-Aldrich, Qualigens, Loba-Chemi, SD Fine Chem and Central Drug House, India. Seedlings grown in double distilled water served as the controls. The Petri dishes were kept in a growth chamber maintained at 25±0.5°C, 16:8 h dark-light photoperiod (1700 Lux), for a growth period of seven days.

Metal analysis and enzyme assays

After seven days, seedlings were harvested, thoroughly washed with distilled water, dried at 80°C for 24 h and digested in a digestion mixture (H₂SO₄: HNO₃: HClO₄ in 1: 5: 1 ratio) (Allen et al. 1976). The concentrations of metals in the solutions were determined using atomic absorption spectrophotometer (Model AA 6200, Shimadzu, Japan).

One gram of fresh seedlings was homogenized in 3 ml of 100 mM potassium phosphate buffer, pH 7.0, containing 1% (w/v) insoluble polyvinylpyrrolidone (Polyclar-AT, Sigma-Aldrich) in an ice chilled pestle and mortar. The homogenates were centrifuged at 15,000 g for 20 min at 5°C and the supernatants were used for assaying the activities of antioxidative enzymes.

Superoxide dismutase (EC 1.15.1.1)

Superoxide dismutase was estimated after Kono (1978). The method is based on the inhibitory effects of SOD on the reduction of nitroblue tetrazolium (NBT) by superoxide radicals, generated by the autooxidation of hydroxylamine hydrochloride. The reduction of NBT was followed by an absorbance increase at 540 nm. The reaction mixture containing 1.8 ml sodium carbonate buffer (pH 6.0), 750 µl NBT and 150 µl triton X-100 was taken in the test cuvette and the reaction was initiated by the addition of 150 µl hydroxylamine hydrochloride. 70 µl of the enzyme extract was added after 2 min and the percentage inhibition in the rate of NBT reduction was recorded as increase in the absorbance at 540 nm.

Hydroxylamine hydrochloride is auto-oxidized to nitrite by superoxide radicals. The addition of NBT induces an increase in the absorbance at 540 nm due to the production of blue formazon. With the addition of superoxide enzyme, superoxide radicals get trapped, and hence there is an inhibition of reduction of NBT to blue formazon formation. The percentage inhibition of NBT reduction was calculated as:

One unit of the enzyme activity is defined as the enzyme concentration required for inhibiting the absorbance at 540 nm of chromogen production by 50% in 1 min under the assay conditions.

Guaiacol peroxidase (EC 1.11.1.7)

The activity of GPX was estimated according to the method given by Putter (1974). GPX catalyzes the decomposition of H₂O₂ of a large number of organic compounds, such as phenols, aromatic amines, hydroquinones etc., and in particular pyrogallol, guaiacol etc. These cause the reduction of H₂O₂ to water and oxygen using guaiacol as a substrate.

The reaction is given as:

One mole of H₂O₂ oxidizes one mole of donor (DH₂) (guaiacol) and results in oxidized donor (D). The rate of formation of oxidized guaiacol was followed spectrophotometrically at 436 nm. The reaction mixture comprising 3 ml phosphate buffer (pH 7.0), 50 µl guaiacol solution, 100 µl enzyme sample and 30 µl H₂O₂ solution was taken in the test cuvette. The rate of formation of guaiacol dehydrogenation product (GDPH) was followed spectrophotometrically at 436 nm. One unit of enzyme activity was defined as the amount of enzyme catalyzing the formation of 1 µM of GDPH min g⁻¹ fw. Enzyme activity was calculated as follows:

where extinction coefficient=25.5 mM⁻¹cm⁻¹

Catalase (EC 1.11.1.6)

Catalase activity was determined as per the method of Aebi (1974). Catalase catalyzes the decomposition of H₂O₂ to water and oxygen. The rate of decomposition of H₂O₂ was followed by decrease in absorbance at 240 nm of the reaction mixture.

Catalase activity can be measured by following either the decomposition of H₂O₂ or the liberation of O_2. H₂O₂ shows a continual increase in absorption with decreasing wavelength in the UV range. The difference in extinction per unit time is the measure of catalase activity. The reaction mixture was prepared using 1.5 ml potassium buffer (100 mM, pH 7.0), 1.2 ml H₂O₂ (150 mM) and 300 µl of enzyme extract. The decrease in absorption per min was recorded at 240 nm spectrophotometrically. Enzyme activity was determined using extinction coefficient 6.93×10⁻³ mM⁻¹cm⁻¹. One unit of enzyme activity was calculated as the amount of enzyme required to release half the peroxide oxygen from H₂O_2. Unit activity and specific activity were calculated as for GPX.

Ascorbate peroxidase (EC 1.11.1.11)

The activity of ascorbate peroxidase was estimated according to the method proposed by Nakano and Asada (1981). Ascorbate peroxidase is very specific and one of the most important enzymes in plants. It catalyzes the reduction of H₂O₂ using the substrate ascorbate.

One mole of H₂O₂ oxidizes one mole of ascorbate to produce one mole of dehydroascorbate. The rate of oxidation of ascorbate was followed by decrease in absorbance at 290 nm. Three ml of reaction mixture consisted of 1.5 ml phosphate buffer (100 mM, pH 7.0), 300 µl of 5 mM ascorbate, 600 µl of 0.5 mM H₂O_2, and 600 µl enzyme extract. The decrease in absorbance was recorded at 290 nm. One unit of enzyme activity was determined as the amount of enzyme required to oxidize 1 µM of ascorbate min⁻¹ g⁻¹ fw. Unit and specific activities were calculated as for GPX with an extinction coefficient of 2.8 mM⁻¹cm⁻¹.

3.2.2.8 Glutathione reductase (EC 1.8.1.7)

Glutathione reductase activity was measured using the method proposed by Carlberg and Mannervik (1975). Glutathione reductase catalyzes the reduction of glutathione disulphide (GSSG) to sulfhydryl form (GSH) involving the oxidation of NADPH. The reaction was carried in phosphate buffer (50 mM, pH 7.6).

In this reversible reaction, reduced glutathione is strongly favored and the catalytic activity is measured by following the decrease in absorbance due to the oxidation of NADPH. One unit of enzyme activity was determined as the amount of enzyme required to oxidize 1 µM of NADPH min⁻¹g⁻¹ fw. Unit activity and specific activity were calculated as for GPX with an extinction coefficient of 6.22 mM⁻¹cm⁻¹.

Statistical analysis

The data were analyzed for descriptive statistics, ANOVA, Tukey's multiple comparison tests, multiple regression analysis and path analysis (Sokal and Rholf 1981; Bailey 1995) using self-coded software in MS-Excel. The binary interaction models developed using multiple regression technique were:

where, Y is the studied parameter, X ₁ and X ₂ are metals in binary combination, and b ₁ and b ₂ are partial regression coefficients due to the effects of X ₁ and X ₂ , respectively, and b ₃ is the partial regression coefficient due to interaction between X ₁ and X ₂ . Unitless β-regression coefficients, β _1, β ₂ and β ₃ were computed to determine the relative effects of independent variables, X ₁ , X ₂ and interaction between X ₁ and X ₂ as follows:

where S _X and S _Y are standard deviations of X and Y. Metal interaction was interpreted as described in Table 1 (Bala and Thukral 2008, 2010; Kaur et al. 2009a,b, Kaur et al. 2010).

Table 1. Binary metal interactions in terms of β-regression coefficients.

Interaction	β1	β2	β3		β1	β2	β3
Synergistic (S)	+	+	+		−	−	−
Antagonistic (A)	+	+	−	Or	−	−	+
Mixed (M)	+	−	+		+	−	−
Additive (0)	+/−	+/−	0		+/−	+/−	0

Results

Metal uptake

Mn showed maximum uptake, 0.445 mg g⁻¹ dw, whereas Ni was found to be the metal least accumulated (0.135 mg g⁻¹ dw) at 100 mg l⁻¹ treatment (Table 2). In all binary treatments, both the metal ions mutually significantly inhibited the uptake of each other, except for Mn 25: Co 25, Mn 100: Co 100, Mn 100: Co 50, and Co 25: Mn 100. Supplementation of 100 mg l⁻¹ Cr with Mn (25, 50 and 100 mg l⁻¹) reduced the Cr uptake by 66.7%, 62.2% and 68.3%, respectively. Similar trends were observed in binary combinations of Mn with Ni, Co and Cu. Multiple regression interaction models (Table 3) revealed significant correlations between all the binary combinations of Mn and the uptake of respective metal ions. In (Mn+Cr) and (Mn+Ni), both the ions independently as well as their interaction, inhibited the uptake of each other. In (Mn+Co) and (Mn+Cu), although Mn facilitated the uptake of its coexisting ions, both Co and Cu retarded the uptake of Mn by the seedlings. The mixed interactions occurring between these ions decreased uptake of Mn, whereas the decreased uptake of both Co and Cu was due to the antagonistic interactions. Path analysis (Table 4) shows that in all the binary combinations of Mn, the uptake of all the metals was increased due to their own direct effects, maximum being caused by Co (0.99). However, Mn and the corresponding metals in binary combinations had indirect negative interactive effects on the uptake of each other, maximum being due to interaction between Cu and Mn where the uptake of Cu is decreased by Mn by a path coefficient of -0.58. Similarly, Mn directly reduces the uptake of other metals, maximum (-0.61) being due to the direct effect of Mn on Cu uptake. Except for the direct effect of Co on Mn uptake and Cu on Mn uptake, all the metals decreased the uptake of each other both through direct and indirect effects, maximum indirect effect (-0.92) being due to Cu on Mn uptake.

Table 2. Metal uptake (mg•g⁻¹ dw) by the seedlings of B. juncea grown in water cultures containing different binary combinations of Mn with other metals.

	Mn+Cr: Mn uptake (mg•g^−1 dw) (HSD*=0.18)
Treatments (mg•l^−1)	Cr (0)			Cr (25)			Cr (50)			Cr (100)
Mn (25)	0.224	±	0.014	0.165	±	0.006	0.152	±	0.068	0.144	±	0.068
Mn (50)	0.331	±	0.044	0.188	±	0.025	0.170	±	0.019	0.150	±	0.021
Mn (100)	0.445	±	0.050	0.192	±	0.031	0.175	±	0.021	0.150	±	0.056
	Mn+Cr: Cr uptake (mg•g^−1 dw) (HSD*=0.12)
	Mn (0)			Mn (25)			Mn (50)			Mn (100)
Cr (25)	0.100	±	0.014	0.081	±	0.001	0.073	±	0.010	0.060	±	0.035
Cr (50)	0.119	±	0.014	0.084	±	0.012	0.070	±	0.011	0.060	±	0.025
Cr (100)	0.180	±	0.030	0.060	±	0.045	0.068	±	0.025	0.057	±	0.029
	Mn+Ni: Mn uptake (mg•g^−1 dw) (HSD*=0.25)
	Ni (0)			Ni (25)			Ni (50)			Ni (100)
Mn (25)	0.224	±	0.014	0.203	±	0.070	0.213	±	0.020	0.156	±	0.006
Mn (50)	0.331	±	0.044	0.228	±	0.050	0.189	±	0.080	0.131	±	0.020
Mn (100)	0.445	±	0.050	0.523	±	0.054	0.425	±	0.070	0.308	±	0.129
	Mn+Ni: Ni uptake (mg•g^−1 dw) (HSD*=0.08)
	Mn (0)			Mn (25)			Mn (50)			Mn (100)
Ni (25)	0.094	±	0.026	0.088	±	0.010	0.077	±	0.020	0.088	±	0.013
Ni (50)	0.135	±	0.045	0.085	±	0.025	0.068	±	0.015	0.086	±	0.010
Ni (100)	0.135	±	0.022	0.069	±	0.001	0.081	±	0.001	0.079	±	0.019
	Mn+Co: Mn uptake (mg•g^−1 dw) (HSD*=0.20)
	Co (0)			Co (25)			Co (50)			Co (100)
Mn (25)	0.224	±	0.014	0.294	±	0.014	0.209	±	0.008	0.203	±	0.030
Mn (50)	0.331	±	0.044	0.285	±	0.013	0.245	±	0.071	0.182	±	0.054
Mn (100)	0.445	±	0.050	0.632	±	0.050	0.515	±	0.080	0.430	±	0.081
	Mn+Co: Co uptake (mg•g^−1 dw) (HSD*=0.18)
	Mn (0)			Mn (25)			Mn (50)			Mn (100)
Co (25)	0.110	±	0.016	0.089	±	0.030	0.097	±	0.001	0.120	±	0.010
Co (50)	0.158	±	0.013	0.099	±	0.011	0.103	±	0.001	0.119	±	0.035
Co (100)	0.224	±	0.060	0.119	±	0.020	0.122	±	0.004	0.126	±	0.005
	Mn+Cu: Mn uptake (mg•g^−1 dw) (HSD*=0.12)
	Cu (0)			Cu (25)			Cu (50)			Cu (100)
Mn (25)	0.224	±	0.014	0.230	±	0.025	0.127	±	0.061	0.096	±	0.020
Mn (50)	0.331	±	0.044	0.213	±	0.020	0.101	±	0.060	0.124	±	0.005
Mn (100)	0.445	±	0.050	0.246	±	0.041	0.218	±	0.023	0.238	±	0.033
	Mn+Cu: Cu uptake (mg•g^−1 dw) (HSD*=0.06)
	Mn (0)			Mn (25)			Mn (50)			Mn (100)
Cu (25)	0.081	±	0.029	0.087	±	0.010	0.075	±	0.020	0.118	±	0.004
Cu (50)	0.168	±	0.006	0.100	±	0.015	0.092	±	0.020	0.087	±	0.008
Cu (100)	0.235	±	0.008	0.103	±	0.010	0.105	±	0.010	0.090	±	0.010
Note: *HSD values of Tukey's multiple comparison test in two-way ANOVA.

Table 3. Multiple regression interaction model for metal uptake (Y) in the seedlings of B. juncea grown in water cultures containing binary combinations of Mn with other heavy metals (X₁+X₂) mg•l⁻¹.

		β regression coefficients			Metal uptake (Y)	β regression coefficients
(X1+X2)	Metal uptake (Y)	β1	β2	β3		β1	β2	β3
Mn+Cr	Mn (r=0.8018**)	0.68	−0.10	−0.74	Cr (r=0.7625*)	0.48	−0.17	−0.68
Mn+Ni	Mn (r=0.9366***)	0.95	−0.22	−0.27	Ni (r=0.5579)	0.33	−0.09	−0.54
Mn+Co	Mn (r=0.9061***)	0.88	−0.23	−0.01	Co (r=0.7181*)	0.99	0.44	−0.97
Mn+Cu	Mn (r=0.8403**)	0.55	−0.61	−0.06	Cu (r=0.7702*)	0.98	0.48	−1.20
Note: Significant at ***p≤0.001, **p≤0.01, *p≤0.05.

Table 4. Path analysis of direct and indirect effects of heavy metals on the uptake of each other in B. juncea seedlings grown in water culture containing binary combinations (X₁+X₂) mg•l⁻¹ of Mn with other heavy metals.

Combination	Effect of	Total effect (Ryx)	Direct effect	Indirect effect- interaction	Effect of	Total effect (Ryx)	Direct effect	Indirect effect-interaction
Mn+Cr	Mn on Mn uptake	0.32	0.68	−0.36	Mn on Cr uptake	−0.67	−0.10	−0.57
	Cr on Cr uptake	0.16	0.48	−0.33	Cr on Mn uptake	−0.69	−0.17	−0.52
Mn+Ni	Mn on Mn uptake	0.82	0.95	−0.13	Mn on Ni uptake	−0.43	−0.22	−0.21
	Ni on Ni uptake	0.07	0.33	−0.26	Ni on Mn uptake	−0.51	−0.09	−0.42
Mn+Co	Mn on Mn uptake	0.87	0.88	−0.01	Mn on Co uptake	−0.24	−0.23	−0.01
	Co on Co uptake	0.52	0.99	−0.47	Co on Mn uptake	−0.30	0.44	−0.74
Mn+Cu	Mn on Mn uptake	0.52	0.55	−0.03	Mn on Cu uptake	−0.66	−0.61	−0.05
	Cu on Cu uptake	0.39	0.98	−0.58	Cu on Mn uptake	−0.44	0.48	−0.92

Antioxidative enzymes

The corresponding data obtained from the enzyme assays of single metal stressed seedlings showed that there is significant enhancement in the activities of antioxidative enzymes, except for CAT (Figure 1). Maximum increase in the activities of all the enzymes was observed in the seedlings treated with Mn 100 mg•l⁻¹ alone. The data further revealed that binary combination of metals also increased the activities of antioxidative enzymes. It was found that Mn+Cu combination increased the SOD and GPX activity to 20.8 and 56.7 mM UA/mg protein, respectively. Similarly APX and GR activities were further increased under the combined influence of Mn+Cr and Mn+Ni, respectively. In the treatment (Mn100+Cr100), maximum APX activity was observed to be 9.5 mM UA/mg protein, and the maximum APX activity (10.9 mM UA/mg protein) was observed in the seedlings treated with (Mn100+Ni100) mg•l⁻¹. Binary interaction model (Table 5) for the relative activities of different antioxidative enzymes as a function of two metals in binary combination derived using multiple regression analysis revealed that there are significant correlations between various enzymatic activities and the metals in binary combinations. Regarding SOD, GPX, APX and GR, it was observed that both the metals independently increased the enzymatic activities as indicated by the positive values of β-regression coefficients, but the interactive effects, caused by the antagonistic interactions occurring between the coexisting metal ions, were negative on the activity of these enzymes. Catalase, however, was decreased under the influence of all the metals eliminating the effects of metals in combination. The interactive effects of metals in binary combinations with Mn were positive.

Figure 1.  Specific activities (mM UA/mg protein) (Mean±SD) of antioxidative enzymes in the seedlings of B. juncea grown in binary combinations of Mn with other heavy metals (mg l⁻¹).

Table 5. β-regression coefficients for the activities of different antioxidative enzymes (Y) in the seedlings of B. juncea grown in water cultures containing binary combinations of Mn (X₁) with other heavy metals (X₂).

		β regression coefficients				β regression coefficients
Enzyme	Metals (X1+X2)	(β1)	(β2)	Interaction (β3)	Metals (X1+X2)	(β1)	(β2)	Interaction (β3)
SOD	Mn+Cr	1.13	0.70	−0.47	Mn+Ni	1.30	0.73	−0.79
SOD	Mn+Co	1.36	0.54	−0.84	Mn+Cu	0.98	0.59	−0.19
GPX	Mn+Cr	1.07	0.85	−0.51	Mn+Ni	1.14	0.93	−0.69
GPX	Mn+Co	1.06	0.94	−0.58	Mn+Cu	1.02	0.65	−0.28
CAT	Mn+Cr	−0.59	−0.99	0.20	Mn+Ni	−0.79	−1.27	0.77
CAT	Mn+Co	−0.83	−1.17	0.64	Mn+Cu	−0.81	−1.36	0.99
APX	Mn+Cr	1.07	0.27	−0.15	Mn+Ni	1.24	0.61	−0.58
APX	Mn+Co	1.39	0.55	−0.97	Mn+Cu	1.20	0.70	−0.58
GR	Mn+Cr	1.34	0.66	−0.83	Mn+Ni	1.07	0.67	−0.36
GR	Mn+Co	1.36	0.91	−1.12	Mn+Cu	1.34	0.88	−1.00

Discussion

The present study revealed antagonistic effects of metals on the uptake of each other. This may be due to the competitive inhibition of heavy metal uptake by the seedlings. It was evident that the uptake of heavy metals induced a strong antioxidative response in B. juncea seedlings by increasing the activities of antioxidative enzymes, except for catalase. Pandey et al. (2005) and Shanker et al. (2004) reported increase in SOD activity in B. juncea and Vigna radiata under Cr (VI) stress. Gao et al. (2008) and Posmyk et al. (2009) also reported that the SOD activity in Jatropha curcas and red cabbage seedlings increased concomitantly with increasing Cu concentrations in the medium. Wang et al. (2004b) attributed Cu-induced increase in SOD activity to Cu being a redox active metal capable of generating harmful free radicals, such as hydroxyl, peroxyl and alkoxyl radicals. The present study did not reveal any significant change in the catalase activity. Wang et al. (2004a) observed significant increase in APX, GPX and SOD, but CAT activity decreased significantly in B. juncea seedlings treated with Cu²⁺. Sharma et al. (2008) also observed reduced CAT activity in B. juncea seedlings under Ni stress. In sunflower roots Cu increased GPX, but SOD and CAT were reduced (Jouili and El Ferjani 2003, 2004). A decrease in CAT activity was also observed in oat leaves after exposure to the toxic Cu concentrations by Luna et al. (1994). Decrease in CAT activity in plants on metal exposure could be attributed to metal ions binding or replacing some components like Fe²⁺ in the enzyme or that the increased H₂O₂ level causes inactivation of the enzyme (Mazhoudi et al. 1997). Also, it may be possible that catalase is more sensitive to metal ions as these readily bind to thiol groups, thereby inactivating the thiol containing enzyme. Feierabend et al. (1992) has already shown that, under stress conditions, inactivation of CAT is linked to H₂O₂ accumulation.

Ascorbate-glutathione cycle in chloroplast is the main component of the defense system for scavenging H₂O₂ that ultimately converts H₂O₂ to H₂O and O₂. This cycle mainly involves APX, POX and GR. It was also reported that in many plant species, excessive uptake of heavy metals, such as Ni, cadmium (Cd) and lead (Pb) induced a strong increase in POX activity (Mazhoudi et al. 1997; Baccouch et al. 2001). APX plays a crucial role in ascorbate-glutathione cycle, scavenging H₂O₂ using ascorbate as a substrate. The induction of APX activity due to Cr in plants is also reported in Vigna radiata (Shanker et al. 2004), and B. juncea (Diwan et al. 2008); due to Cu in B. juncea (Wang et al. 2004b); due to Mn in Cucumis sativus (Shi et al. 2006).

Enhancement in the GPX activity as observed in metal stressed B. juncea seedlings is also coherent with the findings previous workers. Cr induced increase in GPX activity was observed in the leaves of Amaranthas viridis (Liu et al. 2008). Increase in GPX activity in has been reported in different plants due to Cu (Jouili and El Ferjani 2003, 2004; Wang et al. 2004b; Posmyk et al. 2009), Mn (Demirevska-Kepova et al. 2004), Hg (Cho and Park 2000; Narang et al. 2008) and Pb (Ruley et al. 2004).

Since APX eliminates H₂O₂ by converting ascorbate to dehydroascorbate, the increased production of dehydroascorbate is recycled back to ascorbate with the help of GR, thereby catalyzing this last rate limiting step of glutathione cycle. Increased activity of GR under metal stress is reported by many researchers, which is also in agreement with our findings. In the present study, maximum enhancement in the activity of GR (9.56 mM UA mg⁻¹ protein) was observed at the single metal treatment of 100 mg l⁻¹ Mn. Moreover, the binary combination (Mn100+Ni100) caused maximum increase (10.9 mM UA mg⁻¹ protein) in the enzymatic activity. This result is in accordance with the finding of several workers (Prasad et al. 1999; Dixit et al. 2001; Shi et al. 2006; Srivastava et al. 2006; Tanyolac et al. 2007; Diwan et al. 2008; Khan et al. 2009). This increased activity of GR under metal stress can be attributed to the fact that it provides GSH for the synthesis of phytochelatins (Baisak et al. 1994).

The results of the present study suggest that B. juncea seedlings try to counteract high concentration of ROS produced under metal toxicity through a coordinated increase in the activities of enzymes involved in their detoxification. Enhanced enzyme activities under single metal stress in the current study are in broad agreement with many other earlier reports, as it has been shown that these enzymes are triggered by ROS following exposure to metal-induced oxidative stress. However, investigations focusing on the adaptive physiological and biochemical mechanisms of the interactions between different metals are rather scanty. The perusal of literature regarding the antioxidative defense system in plants showed that little information is available with respect to multiple metal stress. In our earlier experiments on the interactive effects of Cr and Ni on the growth and uptake potential of B. juncea seedlings, it was observed that Mn in combination with Cr and Ni caused positive interactive effect on the root-shoot length and dry weight of the seedlings owing to antagonistic interactions occurring between them leading to mutual amelioration of their toxicities which is manifested through better seedling growth as compared to the seedlings grown in single treatments of Cr and Ni, as well as in other binary combinations such as, (Cr+Ni), (Cr+Co), (Cr+Cu), (Ni+Co) and (Ni+Cu) (Kaur et al. 2009a, Kaur et al. 2010). This improved growth in binary combinations involving Mn could be due to increased tolerance of B. juncea by the enhanced activation of antioxidative enzymes, as observed in the present investigation. Moreover, this increase in the activities of antioxidative enzymes under binary metal stress was also observed in binary combinations of zinc (Zn), in our previous experiment (Kaur et al. 2009b). It was observed that of all the binary combinations tested, Zn+Co and Zn+Ni were most effective in increasing the activities of GPX and GR, respectively, whereas Zn+Cu and Zn+Cr increased the activities of APX and SOD, respectively. The increase in the activities of antioxidative enzymes under binary metal stress was attributed to the accelerated operation of the defence mechanism of the plant that confers greater stress tolerance towards combined toxicities of the heavy metals.

Conclusion

The present study establishes that Mn suppresses the toxicities of Cu, Co, Cr and Ni, due to the antagonistic interactions between them, thereby improving seedling growth in B. juncea. Furthermore, B. juncea counteracts the high concentrations of ROS produced under metal toxicity through a coordinated increase in the activities of antioxidative enzymes required for their detoxification.

Acknowledgements

Thanks are due to the Ministry of Environment and Forests, Government of India, for financial assistance (sanction # 19-87/2000-RE).