Arsenic-induced oxidative stress in Brassica oleracea: Multivariate and literature data analyses of physiological parameters, applied levels and plant organ type

Abstract

Plant redox homeostasis governs the uptake, toxicity and tolerance mechanism of toxic trace elements and thereby elucidates the remediation potential of a plant. Moreover, plant toxicity/tolerance mechanisms control the trace element compartmentation in edible and non-edible plant organs as well as the associated health hazards. Therefore, it is imperative to unravel the cellular mechanism involved in trace element toxicity and tolerance. The present study investigated the toxicity and tolerance/detoxification mechanisms of four levels of arsenic (As(III): 0, 5, 25 and 125µM) in Brassica oleracea under hydroponic cultivation. Increasing As levels significantly decreased the pigment contents (up to 68%) of B. oleracea. Plants under As stress showed an increase in H2O2 contents (up to 32%) in roots while a decrease (up to 72%) in leaves because As is mostly retained in plant roots, while less is translocated toward the shoot, as evident from the literature. Arsenic treatments caused lipid peroxidation both in the root and leaf cells. Against As-induced oxidative stress, B. oleracea plants mediated an increase in the activities of peroxidase and catalase. Contradictory, the ascorbate peroxidase and superoxide dismutase activities slightly decreased in the As-stressed plants. In conclusion and as evident from the literature data analysis, As exposure (especially high level, 125µM) caused pigment toxicity and oxidative burst in B. oleracea. The ability of B. oleracea to tolerate As-induced toxicity greatly varied with applied treatment levels (As-125 being more toxic than lower levels), plant organ type (more toxicity in leaves than roots) and physiological response parameter (pigment contents more sensitive than other response variables). Moreover, the multivariate statistical analysis appeared to be a useful method to estimate plant response under stress and trace significant trends in the data set.