ABSTRACT
Excess of copper ion (>2 mg/L) in water is toxic to human beings and the ecosystem. Various water treatment technologies for copper remediation have been investigated in the past. Along with industrial effluents, Bordeaux mixture is also a noteworthy copper contamination source in the agricultural ecosystem. In our study, the biosorbent efficiency of dried orange peel was investigated through an environment-friendly process for the removal of cupric ions. Effects of pH, adsorbate concentration, adsorbent dosage, and temperature for the removal of Cu (II) were studied. Slightly acidic environment (pH = 6) was found to be optimum for removal of copper. The equilibrium data were well fitted with the Langmuir and Freundlich isotherms. The surface morphology of the adsorbent was studied using scanning electron microscope. Crystalline nonhomogenous surface was observed after copper adsorption. Desorption study indicated that 0.1N H2SO4 is the best eluent for the removal of adsorbed copper from the powdered dried orange peel.
Subject(s)
Citrus sinensis , Water Pollutants, Chemical/chemistry , Water Purification , Adsorption , Biodegradation, Environmental , Copper/chemistry , Ecosystem , Hydrogen-Ion Concentration , Kinetics , WaterABSTRACT
The dataset presented in this article is associated with the TMT (Tandem mass tag) labeled proteomics of chili pepper plant (Capsicum frutescens) infested by a broad mite (Polyphagotarsonemus latus). Data was captured using a nano liquid chromatography system coupled with high-resolution Orbitrap FusionTribridmass spectrometer. Proteomics data was analyzed using the Proteome Discoverer version 2.4 tool using MASCOT and SequestHT algorithms. We have identified a total of 5,807 proteins supported by 48,555 unique peptides and 1,279,655 peptide-spectrum matches. Individually, 5,186 proteins were detected in healthy leaf samples, 5,193 in infested leaf sample, 5,194 proteins in healthy meristem sample, and 5,196 proteins in infested meristem samples. Datasets obtained from reciprocal blast against the Arabidopsis thaliana proteome database enabled the prediction of protein-protein interactions, and subcellular localization of differentially expressed proteins, which are also included in this article. Data presented in this article has been deposited in the ProteomeXchange Consortium via the PRIDE repository, which can be accessed through the accession ID: PXD018653.
ABSTRACT
Plant-pathogen interactions are key biological events that shape ecological dynamics, food production, agriculture and economy. In this context, Capsicum frutescens is an economically and culturally significant chili pepper plant grown widely across the globe as an essential ingredient of hot sauces, chili concentrates, oleoresin flavors, and also in traditional medicines. An important pathogen that limits chili cultivation causing low yield and economic loss is the broad mite, Polyphagotarsonemus latus. Broad mite-infested chili plants have stunted growth and leaves appear coppery and dark, which show symptoms of leaf curl and more importantly the smaller fruits unfit for consumption. The molecular mechanisms of how broad mite affect chili remain poorly understood. In this study, we report a tandem mass tag (TMT)-labeled mass spectrometry-based quantitative proteomic analysis of leaves and apical meristems of healthy and infected chili pepper plants. In total, we identified 5799 proteins, of which 1677 proteins were found to be differentially regulated in infested plants. Related signaling pathways of the differentially expressed proteins were examined using bioinformatics tools. Predominantly, we identified pathways associated with jasmonic acid synthesis, mitogen-activated protein kinase, and plant defense and hormone signal transduction. We also observed upregulation of several enzymes of the phenylpropanoid and carotenoid biosynthetic pathways. This study provides the first in-depth proteomic analysis that correlates broad mite infestation in chili and dysregulation of various pathways that take part in plant defense. In the future, data can be extrapolated for innovation in pest management methods whose ecological footprints are better understood.