Application of Genomics and Proteomics in Bioremediation

Application of Genomics and Proteomics in Bioremediation

Amol Uttam Hivrale, Pankaj K. Pawar, Niraj R. Rane, Sanjay P. Govindwar
Copyright: © 2016 |Pages: 16
DOI: 10.4018/978-1-4666-9734-8.ch005
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Abstract

Bioremediation mediated by microorganisms is proving to be cost effective, ecofriendly and sustainable technology. Genome enable experimental and modeling techniques are of a great help in evaluating physiology and enhancing performance of life forms to be used for bioremediation purpose. Similarly, the application of proteomics in bioremediation research provides a global view of the protein composition of microbial cell and offers promising approach to understand the molecular mechanism of removal of toxic material from the environment. Combination of proteomics and genomics in bioremediation is an insight into global metabolic and regulatory network that can enhance the understanding of gene functions. Present chapter give a bird's eye view of genomics and proteomics and their potential utilization in bioremediation and for the clearer understanding of the cellular responses to environmental stimuli. An understanding of the growth conditions governing the expression of proteome in a specific environment is essential for developing rational strategies for successful bioremediation.
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1. Introduction

Bioremediation is a process in which naturally occurring organisms are used for rapid degradation / removal of hazardous pollutants from environment in order to obtain healthy soil, sediments, substances and ground water (Kumar et al., 2011). In natural way biodegradation is the recycling of waste or breaking down organic matter in to nutrients for the other organisms (Alexander, 1994). Bioremediation is carried out with the help of life forms, including bacteria, fungi, insects, worms, plants, etc. by taking nutrients such as C, N and P from the contaminant ultimately transforming xenobiotics in to environment friendly products (Vidali, 2001). Bioremediation approach becomes important when it comes to remediation of water reserves. Industrial effluents especially textile industry waste are responsible for contamination of water bodies which result in limiting the water availability for drinking and agriculture purpose (King et al., 1997).

Microbes and Bioremediation

Dynamic behavior, flexibility in nutritional requirements and ability to adopt under extreme stress conditions makes the microbe the most eligible life forms for survival. This virtue of the microbe is proving to be beneficial to human kind especially when it comes to removal of contaminants / toxic entities from environment. A large number of microorganisms have been reported for degradation of different industrial wastes such as dyes (Sartale et al., 2011, Keharia & Madamwar, 2003); hydrocarbons, specially related to petrochemiscal waste (Chhatre et al., 1996; Kapley et al., 2009; Mishra et al., 2001); tannery effluent (Shrivastava et al., 2003); chlorinated aromatics (Banta and Kahlon, 2007); distillery spentwash (Kumar et al., 2007); pesticides (Malhotra et al., 2007) heavy metals (Tripati & Shrivastava, 2007) and so on. Similarly, a phenomenon such as chemotaxis and its relevance in bioremediation using the pure culture system in model study has also been reported (Paul et al., 2006). The Energy Research Institute, New Delhi, demonstrated an application of carrier based hydrocarbon- degrading bacterial consortium for bioremediation of crude oil contaminated agricultural land in northeastern and western part of India (Mishra et al., 2001). Utilization of genomic tools in the identification of microbial community has led to the discovery of unique bacteria that were not accessible by traditional techniques. DNA extraction from target niches and amplification of the DNA bar-coding region by polymerase chain reaction (PCR) has proved extremely useful in meaningful characterization of microbial community (Malik et al., 2008). Real time microbial community analysis is also possible with the help of sequencing based approach where microbial population dynamics can be studied at different time interval and as function of carbon sources utilized by microbes.

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