1.1 Introduction - Shodhganga : a reservoir of Indian...

Chapter 1: Introduction

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1.1 Introduction

Environment has lost its pristine characteristic since the beginning of industrialization

on land as well as water bodies as a consequence of selfish unending appetite of

Homo sapiens for so called comfort and luxury. In Gujarat, many industrial estates are

situated within the „Golden Corridor‟ (a highly industrialized zone from Vapi to

Mehsana). Industrial units manufacturing dyes, paints and pigments, pharmaceuticals,

textiles, etc release liquid wastes containing dyes, xenobiotic compounds and many

other unnatural products in the environment and thus are the major cause of ground

and surface water pollution in these areas (Fig. 1.1). This has resulted in serious

health problems in workers and slums surrounding these industrial estates. This 20th

century industrialization has now compelled us to think about developing

environmental remediation strategies on the priority basis to save the basic essential

components of life (Shah et al., 2011).

Fig. 1.1: Photograph depicting the industrial discharges, containing dyes, xenobiotic

compounds and many other unnatural products, released in the Khari-cut canal

flowing through the GIDC, Vatva, Ahmedabad, Gujarat, India. Canal bank samples

used in the present study have been collected from this site.


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The initially developed physical and chemical environmental clean-up technologies

are expensive, energy consuming and most importantly are not completely

environment-friendly. Consequently, the focus shifted on developing self-sustainable

and eco-friendly microbial bioremediation technologies. Implementation of

efficacious bioremediation strategies relies heavily on indigenous microbial

community dynamics - structure and function. Microorganisms, the only living beings

to be omnipresent in environment, are the fastest evolving and adapting (Houghton

and Shanley, 1994), and thus most suitable for coping with all the changes in

environment. Consequently, microbial communities, fundamental components of

ecosystems, can play a critical role in the metabolism and detoxification of

anthropogenic/xenobiotic compounds. Microorganisms have sets of catabolic genes,

capable of processing various metabolic pathways, which are integrated in such a

manner that xenobiotic compounds are converted to intermediates which can enter

central metabolism such as kreb‟s cycle, glycolysis and others.

Bioremediation has been successfully applied for oil spill removal, hydrocarbon

degradation, wastewater treatments, metal removal, xenobiotic/recalcitrant compound

degradation and many other such contaminant removal applications (Ham and

Bonner, 1997). Prior to designing bioremediation strategies, it is necessary to inspect

and assess the contaminated site. However, in reality much of the information on

bioremediation strategies is not available and whatever is available is not assembled

and linked with other information. Conversely, it is indeed difficult to compile all

(successful and failed) strategies and compare them, as many factors like climate, soil

characteristics, geological aspects, water levels, aerobic/anaerobic, waste and disposal

facilities and such other parameters play an important role in success or failure of

bioremediation treatments. The variation in environmental factors from location to

location and even from one niche to another niche within a location has lead to a huge

diversity in microorganisms and their capabilities. Yang et al. (2011) studied effects

of soil organic matter on the microbial polycyclic aromatic hydrocarbons (PAHs)

degradation potentials. The microbial activities in humic acid were much higher than

those in humin, which sequesters organic pollutants stronger. The results suggested

that the nutrition support and sequestration were the two major mechanisms that


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influenced the development of microbial PAHs degradation potentials (Yang et al.,

2011). Nutrient requirement, add another dimension in bioremediation, in a sense that

many microorganisms are able to use xenobiotic compounds as sole carbon and/or

nitrogen source. However, many times they cannot survive solely on xenobiotic

compound and require additional carbon and/or nitrogen source for growth and then

by co-metabolism they can transform or degrade the pollutants. Moreover, an

industrially contaminated area is polluted with not just one particular but variety of

contaminants. Consequently, microorganisms should be able to degrade in presence of

all co-contaminants. Ben Said et al. (2008) characterized PAH degrading bacteria

from sediments of the Bizerte lagoon in presence of multiple contaminants such as

organic pollutants, heavy metals and antibiotics. Moreover, a success or a failure of a

strategy in one case/application does not imply that it will be the same way in other

cases/applications. Hence, the utmost priority is to analyse the capabilities of

indigenous microbial populations and based on that information necessary

modifications may be made to fasten the bioremediation process.

Microbial bioremediation strategies can be applied either ex situ or in situ in order to

restore a contaminated environment. Ex situ treatment involves an off-site separate

treatment facility usually a bioreactor or an effluent treatment plant for degradation of

contaminants. In situ treatment involves on-site bioremediation of the contaminants

by monitoring the efficiencies of natural attenuation (progressive removal of

contaminant) and then facilitating the degradation by intentional biostimulation of

indigenous microbial communities (by providing nutrients and electron

acceptors/donors) or bioaugmentation (by adding microbes and nutrients) (Desai et

al., 2010).

Any one particular microorganism is incapable of processing all the metabolic

reactions to degrade environmental pollutants, however a group of diverse organisms

form a community and collectively process all the essential metabolic reactions for

bioremediation. Moreover, more than 99% of the microbes that exist in the

environment cannot be cultivated easily (Schloss and Handelsman, 2005). Thus, most

of the microbes in the environment have not been described and accessed for

biotechnology or basic research. Abundance and diversity of unculturable bacteria in


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almost all environmental niches have led to the understanding that the so-called

„unculturable‟ bacteria actually multiply in their natural environment and if suitable

culture conditions were provided it should be possible to cultivate them in the

laboratory. In the earliest cultivation attempts, media with very low nutrient are used

considering the high nutrient contents of common laboratory media as compared to

those present in the natural environments (Sharma et al., 2005).

One way around this problem is metagenomics, which aims to access the genomic

potential of an environmental sample either directly or after enrichment for specific

purpose. Soil/Marine metagenome can be enriched in various ways keeping in mind

specific gene targets to be screened such as encoding promising biocatalysts, novel

antibiotics and other such targets that have potential applications in bioremediation,

industry, medicine or agriculture. Bacteria capable of xenobiotic degradation are

widely distributed in the environment. These bacteria have evolved to utilize a variety

of compounds that are present in the environment. Hence environmental samples are

considered to be a reservoir of useful enzymes for bioremediation as well as industrial

catalysis.

1.2 Azo dyes and azoreductases

Dyestuff effluents are one of the major pollutants that are released into the

environment. Even very low concentrations of dyes (less than 1 mg/l) can be highly

visible in solutions. Synthetic dyes are very soluble in water and are recalcitrant to

microbial degradation because they contain substituents such as azo, nitro or sulfo

groups. They are frequently found in a chemically unchanged form even after waste-

water treatment and hence they are regarded as pollutants (Pagga and Brown, 1986;

Shaul et al., 1991). Azo dyes are the largest class of dyes and are widely used in the

textile, leather, food, cosmetic and dyestuff manufacturing industries (Anliker, 1979;

Reisch, 1996; Blumel et al., 2002) because of their chemical stability, ease of

synthesis and versatility (Nakanishi et al., 2001). In year 2000, more than 7×105 tons

of these dyes were produced worldwide (Suzuki et al., 2001). Azo dyes are

characterized by the presence of one or more azo groups (-N=N-) and they have

become a great concern in effluent treatment due to their colour, bio recalcitrance and

potential toxicity to animals and human.


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Azoreductases of microorganisms are favorable for the development of

biodegradation systems for such azo dyes, because these enzymes catalyze reductive

cleavage of azo groups (-N=N-) under mild conditions. In addition, bacterial enzymes

can be readily overproduced (Nakanishi et al., 2001). Identification and

overproduction of azoreductase constitute a straightforward approach for the

development of biodegradation systems. The most generally accepted hypothesis for

this phenomenon is that many bacterial strains possess cytoplasmic enzymes, which

act as „azoreductases‟ and transfer electrons via soluble flavins to azo dyes. The

individual strains may attack the dye molecule at different positions or may use

decomposition products produced by another strain for further decomposition. Hence,

it becomes inevitable to identify and characterize the complete dye decolorizing

genetic machinery.

Azo bonds in azo dyes are reduced, under anaerobic conditions, leading to formation

of corresponding amines. Intermediate aromatic amines are further mineralized under

aerobic conditions (Nakayama et al., 1983; Blumel at al., 2002; Chen et al., 2005).

Till now mainly combined anaerobic-aerobic microbial treatments of dye wastes have

been used, due to limited knowledge about microorganisms having oxygen tolerant

enzymes that are involved in decolourization of azo dyes. Aerobic treatment of

dyestuff containing wastewaters possess significant potential, however, the aerobic

metabolism of azo dyes requires specific enzymes (aerobic azoreductases), which

catalyze the NAD(P)H-dependent reduction of azo compounds to the corresponding

amines (Blumel et al., 2002; Chen et al., 2005). Very few aerobic bacteria which can

grow on/with azo compounds have been reported such as azoreductase from Bacillus

sp. OY1-2 (Suzuki et al., 2001) and Xenophilus azovorans (Blumel et al., 2002).

Moreover, Flavobacterium can aerobically degrade 4,4‟-dicarboxyazobenzene ring

(Overney, 1979; Blumel et al., 2002) and Sphingomonas sp. azoreductases can cleave

several sulphonated naphthol and benzol rings (Stolz, 1999).


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1.3 Omic studies in bioremediation

A multilevel molecular biology approach, combining molecular- (including DNA,

mRNA, protein and metabolites), cellular-, individual-, and community- level data

represents a powerful new multi disciplinary approach to decipher complex biological

systems. „Omics‟ platform that have been used in microbial systems biology include

genomics - which determines the sequence of either whole genome or group of

genes; transcriptomics - which measures mRNA transcript levels; proteomics - which

quantifies protein abundance; metabolomics - which determines abundance of small

cellular metabolites; interactomics - which resolves the whole set of molecular

interactions in cells; and fluxomics - which establishes dynamic changes of molecules

within a cell over a time. However no single „omics‟ analysis can fully unravel the

complexities of fundamental microbial ecology. Therefore, integration of multiple

layers of information, the multi„omics‟ approach, is required to acquire a precise

picture of living microorganisms and their metabolisms (Zhang et al., 2010).

1.3.1 Genomics and Metagenomics

Genomics is the analysis of genome of an organism and metagenomics is to access the

total genomic potential of an environmental sample either directly or after enrichment

of indigenous microbial communities. The term „metagenomics‟ (also known as

community genomics, ecogenomics, or environmental genomics) has had the greatest

impact within the last few years as it has enabled to explore the uncultivable

microorganisms and understand their role in various metabolic pathways. Fig. 1.2

shows the flow-chart describing the basic metagenomic approach. Of all the „omic‟

studies, genomics and metagenomics have had the greatest share of focus in the last

two decades, mainly because of rapid advances in several molecular technologies.

These technologies have completely revolutionized the approaches of studies and

have provided enormous information about the microbial genome(s).


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Fig. 1.2: The flow chart depicting basic metagenomic approach (Adapted from Simon

and Daniel, 2009; Meiring et al., 2011).

Over the last few years, the scientific literature has revealed the progressive

emergence of genomic high-throughput technologies in environmental microbiology

and biotechnology and described their possible or already demonstrated applications

to assess biotreatment of contaminated environments (Stenuit et al., 2008).

Cultivation-independent analyses of the microbial community structures at

contaminated sites have augmented to our understanding of the community dynamics,

relative abundance and distribution of microorganisms actively involved in

bioremediation (Desai et al., 2010). The genes responsible for biodegradation

pathways are usually arranged in clusters that comprise: (i) catabolic genes encoding

the enzymatic steps of the catabolic pathway; (ii) transport genes responsible for

active uptake of the compound; and (iii) regulatory genes that adjust expression of the

catabolic and transport genes to the presence of the compound to be degraded (Diaz

and Prieto, 2000; Diaz, 2004).

However, two technologies - amplification (PCR) and sequencing need a special

mention as they have conferred the major thrust to genomics and metagenomics. As a

corollary of revolutionary developments in high-throughput DNA sequencing

technologies, more than thousand microbial genomes from almost all known major


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phylogenetic lineages have been fully sequenced, many are nearing completion and

many more are regularly initiated all over the world. The computational-based

annotation and comparative genomic analyses of DNA sequences have provided

biologists with information regarding gene function, genome structures, biological

pathways, metabolic and regulatory networks, and evolution of microbial genomes,

which has greatly enhanced our understanding of microbial metabolism (Schoolnik,

2001; Ward and Fraser, 2005; Sharan and Ideker, 2006; Cardenas and Tiedje, 2008;

Rocha, 2008; Zhang et al., 2010). Maphosa et al. (2010a) in their review have

discussed „Ecogenomics‟ - the application of genomics to ecological and

environmental sciences for defining phylogenetic and functional biodiversity at the

DNA, RNA and protein levels. They have described the potential of ecogenomics

approaches in developing high-throughput methods for detecting and monitoring

organohalide respirers, and for providing improvements to selection, specificity and

sensitivity of target biomarkers and their application to evaluate bioremediation

strategies. This knowledge helps to elucidate functions and interactions of organisms

at the ecosystem level in relation to ecological and evolutionary processes.

A significant achievement in the field of microbial ecology was the finding of highly

conserved as well as variable gene sequences that are present in all microorganisms,

most notable of them is the 16S rRNA gene, which has been considered as a „gold

standard‟ for characterizing phylogenetic affiliations of microorganisms that comprise

microbial communities (Lane et al., 1985; Amann et al., 1995; Lovley, 2003). The

pioneering work of Carl Woese using 16S rRNA gene as „evolutionary chronometers‟

in deducing the bacterial phylogeny changed the way we visualize the microbial

world (Larsen et al., 1993). 16S rDNA is the most commonly used phylogenetic

anchor because of its unique base pair composition, with a mosaic of highly variable

and conserved regions, convenient size (smaller than 23S rRNA and larger than 5S

rRNA), optimum size to cover polymorphisms, no lateral gene transfer and large

sequence databases. Moreover, the amplification of a taxonomically diverse collection

of 16S rRNA genes is possible with a small number of primers (Weisburg et al.,

1991). The 16S rRNA gene sequence displays an alternating pattern of conserved and

hypervariable regions reflecting the functional importance of the conserved regions in


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the gene product‟s secondary and tertiary structure. All 16S rRNA genes share nine

hypervariable regions (Neefs et al., 1990), the locations of which are summarized in

Fig. 1.3. Therefore, by analysing 16S rRNA sequences in contaminated environments

it is possible to determine definitively the phylogenetic placement of the

microorganisms that are associated with bioremediation processes (Watanabe and

Baker, 2000; Lovley, 2003; Rogers and McClure, 2003).

Fig. 1.3: Representation of hypervariable regions within the 16S rRNA gene. The

plotted blue line reflects fluctuations in variability amongst aligned 16S rRNA gene

sequences; peaks reflect greater conservation, while troughs correspond to the known

hypervariable regions V1 to V9, also indicated by the red bars.

Nucleic acid based molecular techniques such as denaturing gradient gel

electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), terminal

restriction fragment length polymorphism (T-RFLP), amplified ribosomal DNA

restriction analysis (ARDRA), random amplified polymorphic DNA (RAPD),

amplified fragment length polymorphism (AFLP), automated rRNA intergenic spacer

analysis (ARISA) and single strand conformation polymorphism (SSCP) are used in

conjunction with fluorescence in situ hybridization, PCR technologies, sequencing

techniques and microarrays to monitor the changes in bacterial community and

provide insights on up/down regulations of genes, microenvironment of the sites and

impact of nutrients on bioremediation. These facts in turn assist in process

optimization, validation and understanding the impact of designed bioremediation

strategies on the ecosystem. Fluorescence in situ hybridization in conjunction with


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microautoradiography establishes both phylogenetic as well as functional links of the

species involved in the process of bioremediation (Rogers et al., 2007). DNA stable

isotope probing (DNA-SIP) is another technique used in conjunction with

metagenomics to establish links between microbial identity and particular metabolic

functions. The combination of DNA-SIP and metagenomics not only permits the

detection of rare low-abundance species but also facilitates the detection of novel

enzymes and bioactive compounds (Chen and Murrell, 2010). Ni Chadhain et al.

(2006) used a combination of techniques to study degradation of polycyclic aromatic

hydrocarbons (PAHs) by bacteria. They tracked dioxygenase gene population shifts in

soil enrichment cultures following exposure to naphthalene, phenanthrene or pyrene.

Molecular monitoring of the enrichment cultures before and after PAH degradation

using denaturing gradient gel electrophoresis and 16S rRNA gene libraries provided

information about specific phylotypes of bacteria that were associated with the

degradation of each PAH (Ni Chadhain et al., 2006). Mateos et al. (2006) have strains

of Corynebacterium glutamicum that are resistant to arsenite (up to 60 mM) and using

genetic manipulation tools they are attempting to obtain C. glutamicum mutant strains

which are able to remove arsenic from contaminated water.

Recent advances in molecular techniques, including high-throughput approaches such

as microarrays and metagenomics, have opened up new perspectives and pointed

towards new opportunities in pollution abatement and environmental management.

The current potential of microarrays and metagenomics is capable to investigate the

genetic diversity of environmentally relevant microorganisms and identify new

functional genes involved in the catabolism of xenobiotics (Eyers et al., 2004). The

development of microbial ecological DNA microarrays has enabled researchers to

simultaneously analyze thousands of phylogenetic links and functional genes in order

to characterize microbial communities involved in bioremediation (He et al., 2007).

Microarrays, developed on the basis of sequence analysis, have been used to monitor

genes involved in 2,4-dichlorophenoxyacetic acid (2,4-D) degradation in

environmental samples (Dennis et al., 2003). Loy et al. (2005) used a 16S rRNA

gene-targeted oligonucleotide microarray (RHC-PhyloChip) for cultivation-

independent diversity analysis of betaproteobacterial order „Rhodocyclales‟ in


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activated sludge from an industrial wastewater treatment plant. Functional gene arrays

(FGA) are constructed using PCR amplified products or oligonucleotides derived

from functional genes and are used to identify genes encoding key enzymes involved

in various ecological and environmental processes such as carbon fixation,

nitrification, denitrification, sulphate reduction and contaminant degradation (Rhee et

al., 2004). He et al. (2007) developed GeoChip, a comprehensive microarray

containing 24,243 oligonucleotide (50 mer) probes and covering more than 10,000

genes in more than 150 functional groups and in 2010 they (He et al., 2010) have

developed a new generation gene array, GeoChip 3.0 with around 28,000 probes

covering approximately 57,000 gene variants from 292 functional gene families

involved in nitrogen, carbon, sulphur and phosphorous cycling, metal reduction and

resistance, and organic contaminant degradation. GeoChip 3.0 is a high-throughput

powerful tool for tracking the dynamics of microbial community, functional structure

and linking microbial communities to ecosystem processes and functioning in in situ

bioremediation study. Consequently these techniques can be used in bioremediation

studies to identify microbial populations in ecosystems, assess shifts in microbial

populations due to xenobiotic compounds, measure changes in expression levels

(gene expression profiling), co-relate and compare genomes, identify genes encoding

enzymes, detect mutations and single nucleotide polymorphisms (SNPs).

In detailed description of two metagenomic approaches: Metagenomic libraries and

Sequencing technologies, has been given in the following sub sections. However, the

first and foremost step in metagenomic analyses consists of isolating high molecular

weight DNA from environmental samples in an unbiased manner. Many reports have

highlighted the challenges in obtaining high quality (free from interference of co-

contaminants like humic acids in case of soil) DNA from polluted sites (Fortin et al.,

2004, Desai and Madamwar, 2007). Methods for nucleic acid extraction from soil,

however, suffer from compounded inefficiencies including incomplete cell lysis,

DNA sorption to soil surfaces, co-extraction of enzymatic inhibitors from soil, and

loss, degradation, or damage to DNA (Miller et al., 1999). In the initial efforts to

extract DNA from sediments and soils, scientists used either cell extraction (recovery

of cells from the soil matrix prior to cell lysis) or direct lysis within the soil matrix


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(Holben et al., 1988; Ogram et al., 1987; Steffan et al., 1988). Direct lysis techniques

have been used more because they yield more DNA and presumably a less biased

sample of the microbial community diversity than cell extraction techniques (Holben

et al., 1988; Leff et al., 1995; Steffan et al., 1988). A large number of methods have

been published for the extraction of total microbial community DNA from soils and

sediments (Picard et al., 1992; Holben, 1994; Zhou et al., 1996; Burgmann et al, 2001;

Kauffmann et al., 2004; Bertrand et al., 2005; Desai and Madamwar, 2007).

1.3.1.1 Metagenomic libraries

Metagenomic libraries are prepared by cloning of DNA fragments isolated from an

environmental sample in a suitable vector [e.g. plasmid, phage, fosmid, cosmid, or

bacterial artificial chromosome (BAC)], which is then transformed into a suitable host

strain. Two approaches, the function-driven analysis and the sequence-driven

analysis, have emerged to extract biological information from metagenomic libraries.

The function driven analysis is based on identification of clones that express the

desired trait, followed by characterization of the active clones by sequence and/or

biochemical analysis. The limitations of this approach are that it requires expression

of the function of interest in the host cell and clustering of all of the genes required for

the function. It also depends on the availability of a high-throughput assay for the

function of interest that can be performed efficiently on vast libraries, because the

frequency of active clone is quite low. Many approaches such as improved systems

for heterologous gene expression with shuttle vectors that facilitate screening of the

metagenomic DNA in diverse host species and modifications of Escherichia coli to

expand the range of gene expression are being developed to alleviate these limitations

(Schloss and Handelsman, 2003). Conversely, the sequence-driven analysis relies on

the use of conserved DNA sequences to design hybridization probes or PCR primers

to screen metagenomic libraries for clones that contain sequence of interest.

Significant discoveries have also resulted from random sequencing of metagenomic

clones. However, there has been disagreement about the utility of random sequencing

of metagenomic clones as according to some scientists this approach is too undirected

to yield biological understanding, while others point out that there is very little

information about some divisions of bacteria that any genomic sequence is helpful in


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designing of experiments to discover their characteristics (Schloss and Handelsman,

2003).

Brennerova et al. (2009) revealed the diversity and abundance of meta-cleavage

pathways in microbial communities from soil highly contaminated with jet fuel

(aliphatic and aromatic hydrocarbons) under air-sparging bioremediation using

metagenomics. Moreover, the extradiol dioxygenase diversity was assessed by

functional screening of a fosmid library in Escherichia coli with catechol as substrate.

Kim et al. (2010) prepared a metagenomic library in fosmid vector from a completely

fermented compost and functionally screened and characterized a novel family VIII

alkaline esterase. The functional screening of metagenomic libraries have led to the

discovery of novel genes encoding polyphenol oxidase (Beloqui et al., 2006), ester

and glycosyl hydrolase (Ferrer et al., 2005) and also given indications on the diversity

of extradiol dioxygenases in coke plant wastewater (Suenaga et al., 2007).

1.3.1.2 Sequencing technologies

Sequencing of DNA and/or RNA of humans and many other animals, plants and

microbes have revolutionized the biological research. Sanger developed the chain

termination method in 1975 and two years after that Maxam and Gilbert developed a

method based on chemical modifications. The major revolution started only after the

improvements in chain-termination method. Conversely, automated analysis played

the biggest role as it made the sequencing easier and faster. Capillary electrophoresis

replaced slab gel electrophoresis and became an integral part of automated sequencing

analysis.

Metagenomic studies using first-generation methods included open oceans (Venter et

al., 2004), coastal waters (Culley et al., 2006), coastal stromatolites (Goh et al., 2009),

insect guts (Warnecke et al., 2007) and acid mine biofilms (Tyson et al., 2004). There

are numerous reports on sequencing of complete genome or functionally essential

genes involved in metabolism of biodegradation (Nelson et al., 2002; Kim et al.,

2008; Mattes et al., 2008; Desai et al., 2010). The genome sequence of Pseudomonas

sp. has revealed details about oxygenases, oxidoreductases, ferredoxins and

cytochromes, dehydrogenases, sulfur metabolism proteins and many others. Moreover


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many operons coding for the metabolism of a large number of aromatic compounds

and gene clusters encoding for enzymes which are predicted to be involved in the

metabolism of non-natural substrates were found (Nelson et al., 2002). Desai et al.

(2009) tracked the influence of long-term chromium pollution on soil microbial

communities by analysing 16S rRNA gene clone libraries and observed community

shifts from Proteobacteria to Firmicutes at chromium polluted sites.

However, the era‟s demand of high throughput sequencing at low costs led to constant

upgradation of technologies. Next generation sequencing has propelled biological

research (Schuster, 2008). Today a number of commercial next-generation DNA

sequencing systems are available, namely: Roche‟s 454 GS FLX Genome Analyzer

(Pyrosequencing) (Margulies et al., 2005), Illumina‟s Solexa sequencer (Bentley at

al., 2008), Applied Biosystem‟s SOLiD system (McKernan et al., 2009), Helicos

HeliScope (Shendure and Ji, 2008), Complete Genomics (Drmanac et al., 2010) and

many more are coming in near future such as Ion-Torrent Semiconductor sequencing

(www.iontorrent.com), Pacific Biosciences SMRT (www.pacificbiosciences.com) and

Oxford Nanopore sequencing (www.nanoporetech.com). Walshaw et al., (2011) have

described next-generation sequencing approaches to metagenomics in detail.

1.3.1.2.1 Pyrosequencing

The most mature technology available is Roche‟s 454 system which first became

available in 2005 and even then was capable of producing fifty times the data at one

sixth of the operating cost (Schuster, 2008; Walshaw et al., 2011). Pyrosequencing,

based on „sequencing by synthesis‟ principle, involves sequencing the single stranded

DNA by synthesizing the complementary strand and detection of pyrophosphate

released on addition of each nucleotide. The templates for pyrosequencing are made

either by solid phase template preparation (streptavidin-coated magnetic beads) or

enzymatic template preparation (apyrase and exonuclease). The templates are

hybridized to a sequencing primer and incubated with the enzymes DNA polymerase,

ATP sulfurlyase, luciferase and apyrase, and with the substrates adenosine 5´

phosphosulfate (APS) and luciferin. Detection of pyrophosphate, liberated during

DNA synthesis, is possible by cascade of enzymatic reactions followed by detection

with photodiode, photomultiplier tubes, or a charge-coupled device (CCD) camera.


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Currently, a limitation of the method is that the lengths of individual reads of DNA

sequence are in the neighborhood of 300-500 nucleotides, shorter than the 800-1000

obtained with chain termination methods.

Metagenomes sequenced by next-generation technologies include bulk soil (Leininger

et al., 2006), mycorrhizae (Lumini et al., 2009), coral reefs (Wegley et al., 2007), the

human gut and stools (Qin et al., 2010), mammoth bone (Huson et al., 2007). Lee et

al. (2010) characterized reductive dechlorinating activities and population dynamics

in tidal flat sediments (where tetrachloroethene (PCE) and trichloroethene (TCE) are

common groundwater contaminants) using latest titanium pyrosequencing along with

16S rRNA gene clone libraries and dechlorinator-targeted quantitative real-time PCR

(qPCR). Nowadays, metagenome sequencing is generating a wealth of data and as a

result there is a continuous augmentation of information in databases. More and more

sequences are phylogenetically affiliated and the sequences are being annotated for

more and more number of genes. Besides functional analysis, many research groups

are also focussing on phylogenetically linking the functional genes. However, 16S

rRNA remains the most valuable phylogenetic marker, but this kind of approaches -

phylogenetic affiliation of functional genes will be of immense value in

characterization of bacterial communities.

Metagenomics is a burgeoning area that is generating enormous amounts of biological

information. In addition, the development of new bioinformatics approaches and tools

is allowing innovative mining of both existing and new data (Ward, 2006). The

metagenomic approach has also demonstrated that novel metabolic genes play an

important role in the biodegradation of the compounds. However metagenomics is

still a developing technology with limitations to be overcome, such as gene

enrichment, DNA extraction, host-vector design, library preparation, screening and

the phylogenetic affiliation of isolated genes.

Metagenomic analyses in bioremediation entail: (A) Identification of genes involved

in bioremediation; (B) Characterization of genes and complete genomes; (C)

Phylogenetic affiliation of key catabolic genes.


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Conversely, sheer detection of a gene or a cluster of genes does not bequeath with a

complete picture as the informations about the mRNA expression level, the amount of

protein produced and its location, biological activity or functional relationship with

metabolomes are required for understanding any metabolic reaction. Moreover in

cells many levels of regulation occur after genes have been transcribed, such as post

transcriptional, translational and post translational regulation and all forms of

biochemical control such as allosteric or feedback regulation. Hence, to fully

elucidate microbial metabolism and its responses to environmental factors, it is

necessary to include functional characterization and accurate quantification of all

levels of gene products, mRNA, proteins and metabolites, as well as their interaction

(Zhang et al., 2010).

1.3.2 Transcriptomics and Metatranscriptomics

The genes for bioremediation may be present but not expressed. Therefore there has

been an increased emphasis on quantifying the levels of mRNA for key

bioremediation genes. Transcriptomics is analysis of transcripts of a single organism

and metatranscriptomics is analyses of transcripts of the entire interacting community.

Transcriptomics, also called as gene expression profiling, provides the insights into

the up- or down- regulation of genes under stress conditions in environmental

microbial communities. The first regulatory point for successful synthesis of a protein

from a gene is regulation of gene expression, one of the key processes for adapting to

sustain in affected environmental conditions. Often increased mRNA concentration

can be at least qualitatively associated with higher rates of contaminant degradation

(Schneegurt and Kulpa, 1998; Lovley, 2003). Although gene expression is affected by

many environmental factors, a subset of genes with altered expression can inform on

stress responses. The potential utility here is to improve biomarker identification and

to identify patterns of gene expression associated with different types of pollutants.

Analysis of the mRNA concentration for genes other than those directly involved in

bioremediation might yield additional insights into the factors that control the rate and

extent of bioremediation (Lovley, 2003). Transcriptome analyses have mainly been

dependent on technologies such as PCR, microarray and sequencing. Highly sensitive

methods that can detect mRNA for key bioremediation genes in single cells are now


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available (Bakermans and Madsen, 2002). Conversely, the only major precaution

required is that RNA have a very short life (some transcripts have life of less than a

minute). Consequently, RNA storage after sample collection and preparation is very

important step and strategies for transcriptome analysis need to be designed

accordingly.

Bordenave et al. (2009) applied RT-PCR for analysis of differentially expressed

cDNA involved in microbial mat response after heavy fuel contamination. He et al.

(2010) carried out metatranscriptomic analysis of gene expression and regulation of

Candidatus Accumulibacter enriched lab-scale sludge during enhanced biological

phosphorus removal (EBPR) using medium density oligonucleotide microarrays.

They analysed both aerobic and anaerobic phases and detected the expression of a

number of genes involved in the carbon and phosphate metabolisms, as proposed by

EBPR as well as novel genes discovered through metagenomic analysis. Ozsolak et

al. (2009) reported direct single molecule RNA sequencing without prior conversion

of RNA to cDNA. They applied this technology to sequence femtomole quantities of

poly(A) Saccharomyces cerevisae RNA using a surface coated with poly(dT)

oligonucleotides to capture the RNAs at their natural poly(A) tails and initiate

sequencing by synthesis. They observed transcript 3‟ end heterogeneity and

polyadenylated small nucleolar RNAs. This study provides a path to high-throughput

and low-cost direct RNA sequencing and achieving the ultimate goal of a

comprehensive and bias-free understanding of transcriptomes.

Metatranscriptome analyses in bioremediation entail: (A) Identification of

differentially expressed mRNA and their respective genes; (B) Assessing changes in

the expression of previously characterized key genes (biomarkers).

1.3.3 Proteomics and Metaproteomics

Proteomics is analyses of proteins of a single organism and metaproteomics is to

analyse the protein expression profile of the whole community. Proteins, the major

components of cell, serve as catalytic enzymes in metabolic pathways and in signal

transduction of regulatory pathways of cells (Graham et al., 2007; Zhang et al., 2010).

The cellular expression of proteins in an organism varies with environmental


19

conditions. The changes in physiological response may occur due to the organism‟s

adaptive responses to different external stimuli such as presence of toxic chemicals in

the environment. Developments in functional proteomics approach for environmental

remediation will further pave the way towards cell free bioremediation (Singh and

Nagaraj, 2006). At present, amongst all the omic technologies, metaproteomic

analyses still faces lots of practical challenges. There is no common extraction

procedure for all different (hydrophilic/hydrophobic/etc.) kinds of proteins and

consequently today‟s technologies are capable of resolving only a minute fraction of

metaproteome of complex environments. Substantial improvements in the

technologies for protein extraction, separation and identification are necessary to

encompass a complete picture (Wilmes and Bond, 2006).

Proteome analyses have revolved around two techniques - Two dimensional gel

electrophoresis (2DE) and mass spectrometry (MS). MS has been integrated not just

with 2 DE but also with different chromatographs and various other techniques. Kim

et al. (2009) in their review have given a summary on proteomic approaches used to

study bacterial degradation of aromatic hydrocarbons. Wilmes and Bond (2004)

successfully carried out extraction and purification of the entire proteome from a

laboratory-scale activated sludge system for enhanced biological phosphorus removal,

separation by two-dimensional polyacrylamide gel electrophoresis and mapping of the

metaproteome. Wilmes et al. (2008) applied 2-DE along with MALDI-TOF MS and

Q-TOF MS/MS to identify highly expressed proteins in a mixed culture activated

sludge system and this study provided direct evidence linking the metabolic activities

of „Accumulibacter‟ to the chemical transformations observed in EBPR. D‟Souza-

Ticlo et al. (2009) purified and characterized a thermostable metal-tolerant laccase

having bioremediation potential. The protein Lac IId was purified by 2DPAGE and

analysed by N-terminal sequencing and internal peptide sequencing. Trautwein et al.

(2008) applied 2D-DiGE to quantify the proteomic response of the denitrifying

bacterium Aromatoleum aromaticum strain EbN1 to solvent stress. Wu et al. (2010)

performed systematic analyses at the transcriptomic and proteomic levels to

investigate the expression changes due to high Mn in environment. These studies

revealed that under conditions of increased Mn concentration, there was a change in


20

regulation of proteins involved in virulence, oxidative stress defence, cellular

metabolism, protein synthesis, RNA processing and cell division. Mn regulation of

inorganic pyrophosphatase (Ppa) indicated the potential involvement of phosphate

metabolism in the Mn-dependent oxidative stress defence. Kim et al. (2006) carried

out quantitative proteomic analysis of aromatic pathways in Pseudomonas putida KT

2440 using 2-DE/MS and cleavable isotope-coded affinity tag (ICAT) to determine

whether proteins involved in aromatic compound degradation pathways were altered

as predicted by genomic analysis carried out by Jiménez et al. (2002). The lack of

complete knowledge restricts progress in the site-specific mineralization process. In

the postgenomic era the emphasis is on 2D PAGE and MS to obtain the incomplete

biological information regarding the regulation of growth and metabolism in

microbial communities (Singh, 2006).

Pseudomonas putida is a model organism for bioremediation because of its

remarkable metabolic versatility, extensive biodegradative functions, and ubiquity in

contaminated soil environments. Thompson et al. (2010) characterized proteome

profile of aerobically grown Cr(VI)-stressed Pseudomonas putida F1 in two dissimilar

nutritional environments: rich (LB) media and minimal (M9L) media containing

lactate as the sole carbon source. Comparative analysis indicated that the core

molecular response to chromate, irrespective of the nutritional conditions tested,

comprised seven up-regulated proteins belonging to six different functional categories

including transcription, inorganic ion transport/metabolism, and amino acid

transport/metabolism (Thompson et al., 2010).

Metaproteome analyses in bioremediation entail: (A) Identification of differentially

expressed proteins and their respective genes; (B) Assessing changes in the

abundance of previously characterized key proteins (biomarkers); (C) Protein

structure and function characterization.

1.3.4 Metabolomics, Metametabolomics and Fluxomics

Metabolomics is analysis of entire repertoire of cellular metabolites and

metametabolomics is analyses of metabolites of entire interacting community. Real

time flux analysis of cellular molecules/metabolites within a cell/community over a


21

time period is known as fluxomics (Wiechert et al., 2007). Information on factors

regulating growth and metabolism of microbial communities can be accessed by

metabolomics and fluxomics can provide the missing links in the regulatory

pathways, involved in metabolism of environmental pollutants.

A microbial cell releases a number of low molecular weight primary and secondary

metabolites in response to an environment challenge or stress. The influence of the

local environment on the metabolome of an organism/community can be exploited in

bioremediation to characterize the effects of xenobiotic compounds. Metabolites can

be characterized by mass spectrometry and various spectroscopic techniques. Villas-

Boas and Bruheim (2007) have discussed the scenario of metabolome analysis in

bioremediation. They have described potential of various experimental and conceptual

approaches developed for metabolomics to be applied in bioremediation research,

such as strategies for elucidation of biodegradation pathways using isotope

distribution analysis and molecular connectivity analysis, the assessment

of mineralization process using metabolic footprinting analysis, and the improvement

of the biodegradation process via metabolic engineering. The use of metabolomics

tools can significantly extend and enhance the power of existing bioremediation

approaches by providing a better overview of the biodegradation process (Villas-Boas

and Bruheim., 2007).

Keum et al. (2008) evaluated metabolic profiles of Sinorhizobium sp. C4 using gas

chromatography and mass spectrometry during degradation of phenanthrene in

comparison to natural carbon sources. Tang et al. (2009) performed a fluxomics

analysis on Shewanella sp. known to have co-metabolic pathways for bioremediation

of toxic metals, radionuclides and halogenated organic compounds. From the

metabolic flux analysis of Shewanella sp. using GC-MS and statistical, biochemical

and genetic algorithms they deduced that Shewanella sp. displays a relatively flexible

metabolism fluxes when adapting to different carbon sources. Durand et al. (2010)

applied ex situ Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-

NMR (LC-NMR) as complementary tools to LC-Mass Spectrometry (MS) to define

the metabolic pathway of mesotrione, an herbicide, by the bacterial strain Bacillus sp.

3B6. The complementarities of ex situ and LC-NMR identified six metabolites


22

whereas the structures of only four metabolites were suggested by LC-MS. The

presence of a new metabolic pathway was also evidenced by NMR. The results

demonstrate that NMR and LC-NMR spectroscopy provide unambiguous structural

information for xenobiotic metabolic profiling. Wharfe et al. (2010) used FT-IR as a

metabolite fingerprinting tool for monitoring the phenotypic and biochemical changes

in complex bacterial communities capable of degrading phenol in the activated sludge

from an industrial bioreactor. Wang et al. (2010) applied fourier transform infrared

spectroscopy (FT-IR) and gas chromatography-mass spectrometry (GC-MS) for

analysis of degradation of lube oil and TDOC (total dissolved organic carbon) by a

mixed bacterial consortium. In addition, they analysed that mixed bacterial

consortium can degrade benzene and its derivatives and other aromatic ring organic

matters more than 97%. Maphosa et al. (2010b) used a combination of molecular

diagnostics with mass-balancing and kinetic modeling to improve insight into

organohalide respiring bacteria and metabolite dynamics in an in situ dechlorinating

bioreactor and showed its utility in monitoring bioremediation.

„Meta‟metabolome analyses in bioremediation entail: (A) Identification of differential

metabolic profile; (B) Quantifying functional roles of metabolites.

1.3.5 Bioinformatics - databases, softwares, tools and approaches

Development of bioinformatics techniques has propelled the biological research by a

large magnitude as it has led to automated, precise and faster analyses of the

generated data. Though analysis of the generated data may be the last step, yet it is the

most important, as the wet-lab-data is meaningless, if not completely evaluated. Many

bioinformatic infrastructures are available for analysis of genomics, transcriptomics,

proteomics or metabolomics. The major revolutions have been automated genome

sequencing, genome comparisons to identify the genome function and microbial

evolution, derivation of metabolic and regulatory pathways, gene expression analysis,

statistical tools, data mining techniques to derive protein-protein and protein-DNA

interactions, modeling of 3D structure of proteins and 3D docking between proteins

and biochemicals for rational drug design. The computational-based annotation and

comparative genomic analyses of DNA sequences have provided information

regarding gene function, genome structures, biological pathways, metabolic and


23

regulatory networks, and evolution of microbial genomes, which has greatly enhanced

our understanding of microbial metabolism (Schoolnik, 2001; Ward and Fraser, 2005;

Sharan and Ideker, 2006; Cardenas and Tiedje, 2008; Rocha, 2008; Zhang et al.,

2010).

Despite the fast paced global effort, the current analysis is limited by the lack of

available gene-functionality from the wet-lab data, the lack of computer algorithms to

explore vast amount of data with unknown functionality, limited availability of

protein-protein and protein-DNA interactions, and the lack of knowledge of temporal

and transient behaviour of genes and pathways (Bansal, 2005). However, now the

focus in „in silico‟ developments is to modify the existing tools or develop new tools

to facilitate integrated analyses of „omic‟ studies. The integrated analyses of data sets

will assist in understanding the changes taking place at various levels, beginning from

DNA which contains the basic genetic information, followed by RNA and finally the

products protein or metabolites. Today scientific community is on the verge of using

all this knowledge to understand cellular mechanisms at the systemic level.

Bioinformatics requires the study of microbial genomics, transcriptomics, proteomics,

systems biology, computational biology, phylogenetic trees, data mining and

application of major bioinformatic tools for determining the structures and

biodegradative pathways of xenobiotic compounds (Fulekar and Sharma, 2008).

Bioinformatic resources exclusively for bioremediation studies and analyses have

been and are being developed. The University of Minnesota

Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/) was

developed in 1995 and is regularly updated. It contains information on 1240

compounds, 864 enzymes, 1337 reactions, 274 biotransformation rules and 510

microorganism entries. Besides these data, it includes a Biochemical Periodic Table

(UM-BPT) and a rule-based Pathway Prediction System (UM-PPS)

(http://umbbd.msi.umn.edu/predict/) that predicts plausible pathways for microbial

degradation of organic compounds. Public access to UM-BBD data is increasing and

UM-BBD compound data are now contributed to PubChem and ChemSpider, the

public chemical databases. A new mirror website of the UM-BBD, UM-BPT and

UM-PPS is being developed at ETH Zürich to improve speed and reliability of online


24

access from anywhere in the world (Gao et al., 2010). Urbance et al. (2003) have

developed a freely accessible Biodegradative Strain Database (BSD)

(http://bsd.cme.msu.edu) within the phylogenetic framework of the Ribosomal

Database Project II (RDPII: http://rdp.cme.msu.edu/html) to provide detailed

information on degradative bacteria and the hazardous substances they degrade,

including corresponding literature citations, relevant patents and links to additional

web-based biological and chemical data. Pazos et al. (2003) studied the characteristics

of the global biodegradation network by analysing a large set of chemical reactions

that are implicated in biodegradation to obtain quantitative insights in its organization

and possible mechanism of evolution. Pazos et al. (2005) developed a system,

MetaRouter for maintaining heterogeneous information related to bioremediation in a

framework that allows its query, administration and mining. The system can be

accessed and administrated through a web interface and is available at

http://pdg.cnb.uam.es/MetaRouter and additional material is available at

http://www.pdg.cnb.uam.es/biodeg_net/MetaRouter. Arora et al. (2009) compiled a

database (http://www.imtech.res.in/raghava/oxdbase/) of biodegradative oxygenases

(OxDBase), which provides a compilation of the oxygenase data as sourced from

primary literature in the form of web accessible database. There are two separate

search engines for searching into the database - mono and di oxygenases database,

respectively. Each enzyme entry contains its common name and synonym, reaction in

which enzyme is involved, family and subfamily, structure and gene link and

literature citation. The entries are also linked to several external database including

BRENDA, KEGG, ENZYME and UM-BBD providing wide background information.

At present (September 2011) the database contains information of over 240

oxygenases including both dioxygenases and monooxygenases. OxDBase is the first

database that is dedicated to oxygenases and provides comprehensive information

about them. Due to the importance of the oxygenases in chemical synthesis of drug

intermediates and oxidation of xenobiotic compounds, OxDBase database would be

very useful tool in the field of synthetic chemistry as well as bioremediation. Moriya

et al. (2010) described PathPred (http://www.genome.jp/tools/pathpred/), a web-based

server to predict plausible pathways of multi-step reactions starting from a query


25

compound, based on the local RDM pattern match and the global chemical structure

alignment against the reactant pair library. This server focuses on predicting pathways

for microbial biodegradation of environmental compounds and biosynthesis of plant

secondary metabolites, which correspond to characteristic RDM patterns in 947 and

1397 reactant pairs, respectively. The server provides transformed compounds and

reference transformation patterns in each predicted reaction, and displays all predicted

multi-step reaction pathways in a tree-shaped graph. The transformation of xenobiotic

compounds by microorganisms is essential for the bioremediation of contaminated

environments. There is no single resource that provides information about

environmental contaminants as well as microorganisms with biodegradative

capabilities. A website that consolidates the detailed information about chemical

compound and reference data related to biocatalysis, biotransformation,

biodegradation and bioremediation would be an invaluable tool for academic and

industrial researchers and environmental engineers (Urbance et al., 2003).

Advances in molecular technologies over last few decades have removed the

boundaries between genomics, transcriptomics, proteomics and metabolomics. The

detailed genomic/metagenomic, transcriptomic/metatranscriptomic,

proteomic/metaproteomic, metabolomic/metametabolomic, bioinformatic and other

high-throughput analyses of environmentally relevant microorganisms/environmental

sites provide exceptional and novel insights into key biodegradative pathways at

molecular level and about the ability of organisms to adapt to changes in

environmental conditions, as outlined in Fig. 1.4. Fig. 1.4a presents an overall

framework for integration of molecular and „omic‟ approaches to gain insight into

microbial bioremediation. Fig. 1.4b schematically represents how integrated

molecular analysis can facilitate in understanding bioremediation strategies.


26

a


27

b

Fig. 1.4: (a) Integrating molecular and „omic‟ approaches to gain insight into

microbial bioremediation; (b) A schematic representation describing how integrated

molecular analysis facilitates in understanding bioremediation strategies.

1.4 Milestones in ‘omic’ studies and technologies

There have been numerous discoveries and innovations in technologies which have

changed the course of scientific research. Besides these, even some novel ideas and/or

approaches have given new insights and dimensions to the scientific thoughts. These

technologies and approaches revolutionize not just one specific scientific field of area

or study but all the scientific fields gain benefit. Table 1.1 describes some of the

important milestones that have revolutionized the approach of molecular biology to

study scientific mysteries.


28

Table 1.1: Milestones in „omic‟ studies and technologies.

(A)

Molecular

and ‘omic’

studies

Genomics Fred Sanger and his group

established techniques of

sequencing, genome mapping,

data storage and bioinformatics

analyses. They sequenced

complete genome of virus and

mitochondrion. The first bacterial

genome to be completed was that

of Haemophilis influenza in 1995.

1970s-

1980s

Metabolomics The concept that individuals might

have a metabolic profile was

introduced by Roger Williams in

late 1940s. However, the term

„metabolic profile‟ was introduced

by Horning in 1971.

1971

Bioinformatics The term bioinformatics was

coined by Paulien Hogeweg.

1979

16S rRNA gene The pioneering work of Carl

Woese, using 16SrRNA as

„evolutionary chronometers‟ in

deducing the bacterial phylogeny

changed the way we visualize the

microbial world.

1977

Ribosomal

Database Project

The Ribosomal Database Project

(RDP) provides ribosome related

data and services to the scientific

community, including online data

analysis and aligned and annotated

Bacterial and Archaeal small-

subunit 16S rRNA sequences.

Moreover, the database is

regularly augmented and upgraded

for new sequences and better

analysis.

1992

Proteomics The term proteomics was coined

to make analogy with genomics.

1997

Metagenomics Norman Pace and co-workers were

the first to clone genes directly

from environmental samples in

1991. The term was coined by Jo

Handelsman in 1998.

2000s

Metaproteomics The term metaproteomics was

proposed by Francisco Rodríguez-

Valera to describe the genes

2004


29

and/or proteins most abundantly

expressed in environmental

samples.

Multi‘omics’ Researchers are developing

platforms to integrate all the

studies and technologies.

2005 onwards

(B)

Technologies

Mass

Spectrometry

The first application of mass

spectrometry for analysis of amino

acids and peptides was reported in

1958. Mass spectrometry is a

combination of many

technologies. Many Nobel prizes

in physics and chemistry have

been awarded to studies linked to

mass spectrometry and this in

itself highlights the importance of

this technology.

1958

Two dimensional

gel

electrophoresis

O‟ Farrell developed this

technique and revolutionized

protein purification and

characterization.

1975

Sequencing Maxam-Gilbert and Chain

termination methods developed in

between 1975-1980 were the first

true and feasible sequencing

technologies.

1975-

1980

PCR Kary Mullis developed PCR

technology and this invention

revolutionized the genomic era

and provided a large thrust to

many of the scientific discoveries.

1983

Microarray Microarray evolved from southern

blotting and the use of collection

of distinct DNAs was first time

described in 1987.

1987

Pyrosequencing The technique was developed by

Mostafa Ronaghi in 1996.

However, the major applications

started only after 2005. Robert

Edwards and his colleagues

published the first sequences of

environmental samples generated

with so-called next generation

sequencing, chip-based

pyrosequencing developed by 454

Life Sequences.

1996


30

1.5 Microbial enzymes involved in biodegradation

Enzymes are the catalysts playing a key and direct role in biodegradation of

xenobiotic compounds. Consequently, in the last decade, the focus on enzymes

involved in environmental bioremediation gained considerable importance (Shah et

al., In Press). In this section, the enzyme classes (based on general classification)

involved in bioremediation are described.

1.5.1 Oxidoreductases

Oxidoreductases catalyse the transfer of electrons from one molecule (reductant) to

another molecule (oxidant). In enzyme classification oxidoreductases are classified as

EC 1 and are further subdivided into 22 sub classes. From the UM-BBD we can see

that enzymes belonging to sub classes other than EC 1.9, 1.15, 1.19 and 1.21 play a

role in biodegradation pathways (Table 1.2 – Enzyme sub classes playing role in

bioremediation are in bold).

Table 1.2: Description of oxidoreductases.

Enzyme class Description

EC 1.1 Act on the alcohol (CH-OH) group of donors

EC 1.2 Act on the aldehyde or oxo group of donors

EC 1.3 Act on the CH-CH group of donors

EC 1.4 Act on the CH-NH2 group of donors

EC 1.5 Act on the CH-NH group of donors

EC 1.6 Act on NADH or NADPH

EC 1.7 Act on other nitrogenous compounds as donors

EC 1.8 Act on sulphur as donors

EC 1.9 Act on heme as donors

EC 1.10 Act on diphenols and related substances as donors

EC 1.11 Act on peroxide as an acceptor

EC 1.12 Act on hydrogen as donors

EC 1.13 Act on single donors with incorporation of molecular oxygen

EC 1.14 Act on paired donors with incorporation of molecular oxygen

EC 1.15 Act on superoxide radicals as acceptors

EC 1.16 Oxidize metal ions

EC 1.17 Act on CH or CH2 groups

EC 1.18 Act on iron-sulphur proteins as donors

EC 1.19 Act on reduced flavodoxin as a donor

EC 1.20 Act on phosphorous or arsenic in donors

EC 1.21 Act on X-H and Y-H to form an X-Y bond

EC 1.97 Others


31

Oxidoreductases play a major role in biodegradation pathways. They carry out key

essential steps needed in transformation and mineralization of xenobiotic compounds.

Usually the oxidoreductases are the first enzymes to act on the complex compounds

and hence initiate biodegradation. Mainly oxidoreductases are generally involved in

cellular metabolism, but when under the pressure of xenobiotic compounds they start

utilising them for energy sources and thus in turn are useful for bioremediation

studies. In UM-BBD 878 enzymes are listed that play a role in bioremediation and out

of them 543 belong to oxidoreductases, clearly showing the importance of these

enzymes in biodegradation. Moreover the database also has separate section of 76 and

109 reactions for naphthalene 1,2 dioxygenase and toluene dioxygenase highlighting

the importance of these enzymes. Oxidoreductases can be oxygenases

[monooxygenases and dioxygenases - incorporate molecular oxygen into organic

substrates (classified mainly to 1.13 and 1.14)], reductases [catalyze reductions, in

most cases reductases act similar to oxidases (classified mainly to 1.1, 1.3, 1.4, 1.6,

1.7, 1.8, 1.11, 1.12, 1.20 and 1.97), oxidases [enzymes involved when molecular

oxygen acts as an acceptor of hydrogen or electrons (classified mainly to 1.2, 1.3, 1.4,

1.5, 1.7, 1.8, 1.10 and 1.20)], dehydrogenases [enzymes that oxidize a substrate by

transferring hydrogen to an acceptor that is either NAD+/NADP

+ or a flavin enzyme

(classified mainly to 1.1, 1.2, 1.3, 1.4, 1.5 and 1.17)], peroxidases [catalyzes the

reduction of hydrogen peroxide (1.11)], hydroxylases [add hydroxyl groups to its

substrates(1.14 and 1.97)], laccasses [act on phenols and similar molecules (1.10)]

and others such as hydrogenases (1.12), demethylases (1.13), nitrogenases (1.18),

denitritases (1.7), dehalogenases (1.97), dechlorinases (1.97) and others.

1.5.2 Transferases

Transferases catalyse the transfer of a functional group from one molecule (donor) to

another molecule (acceptor). Generally the donor molecules are coenzymes. In

enzyme classification transferases are classified as EC 2 and are further subdivided

into 9 sub classes. From the UM-BBD we can see that enzymes belonging to sub

classes other than EC 2.2 and 2.9 play a role in biodegradation pathways (Table 1.3 –

Enzyme sub classes playing role in bioremediation are in bold).


32

Table 1.3: Description of transferases.


EC 2.1 Transfer one carbon groups

EC 2.2 Transfer aldehyde or ketone groups

EC 2.3 Acyltransferases

EC 2.4 Glycosyltransferases

EC 2.5 Transfer alkyl or aryl groups other than methyl groups

EC 2.6 Transfer nitrogenous groups

EC 2.7 Transfer phosphorous containing groups

EC 2.8 Transfer sulphur containing groups

EC 2.9 Transfer selenium containing groups

1.5.3 Hydrolases

Hydrolases catalyse the hydrolysis of a chemical bond. In enzyme classification

hydrolases are classified as EC 3 and are further subdivided into 13 sub classes.

Hydrolases are the second most abundant classes of enzymes involved in

bioremediation processes. From the UM-BBD we can see that enzymes belonging to

sub classes EC 3.1, 3.3, 3.5, 3.7, 3.8, 3.10, 3.13 play a role in biodegradation

pathways (Table 1.4 – Enzyme sub classes playing role in bioremediation are in

bold).

Table 1.4: Description of hydrolases.


EC 3.1 Act on ester bonds

EC 3.2 Act on sugars

EC 3.3 Act on ether bonds

EC 3.4 Act on peptide bonds

EC 3.5 Act on carbon-nitrogen bonds

EC 3.6 Act on acid anhydrides

EC 3.7 Act on carbon-carbon bonds

EC 3.8 Act on halide bonds

EC 3.9 Act on phosphorous-nitrogen bonds

EC 3.10 Act on sulphur-nitrogen bonds

EC 3.11 Act on carbon-phosphorous bonds

EC 3.12 Act on sulphur-sulphur bonds

EC 3.13 Act on carbon-sulphur bonds


33

1.5.4 Lyases

Lyases catalyse the breaking of various chemical bonds by means other than

hydrolysis and oxidation and often resulting in formation of a multiple bonds or a new

ring structure. In enzyme classification lyases are classified as EC 4 and are further

subdivided into 7 sub classes. From the UM-BBD we can see that enzymes belonging

to sub classes other than EC 4.6 play a role in biodegradation pathways (Table 1.5 –

Enzyme sub classes playing role in bioremediation are in bold).

Table 1.5: Description of lyases.


EC 4.1 Cleave carbon-carbon bonds

EC 4.2 Cleave carbon-oxygen bonds

EC 4.3 Cleave carbon-nitrogen bonds

EC 4.4 Cleave carbon-sulphur bonds

EC 4.5 Cleave carbon-halide bonds

EC 4.6 Cleave phosphorous-oxygen bonds

EC 4.99 Others

1.5.5 Isomerases

Isomerases catalyze the structural rearrangement of isomers. In enzyme classification

isomerases are classified as EC 5 and are further subdivided into 6 sub classes. From

the UM-BBD we can see that enzymes belonging to sub classes other than EC 5.99

play a role in biodegradation pathways (Table 1.6 – Enzyme sub classes playing role

in bioremediation are in bold).

Table 1.6: Description of isomerases.


EC 5.1 Catalyze racemisation and epimerisation

EC 5.2 Catalyze isomerisation of geometric isomers

EC 5.3 Catalyze intramolecular transfer of electrons

EC 5.4 Catalyze intramolecular transfer of functional group

EC 5.5 Catalyze intramolecular breaking of bonds

EC 5.99 Others


34

1.5.6 Ligases

Ligases catalyze the joining of two molecules by forming a new chemical bond

usually accompanied with hydrolysis. In enzyme classification ligases are classified

as EC 6 and are further subdivided into 6 sub classes. Ligases are enzymes, involved

in joining of molecules and not breaking of molecules required for biodegradation of

xenobiotic compounds. Yet, ligases are occasionally involved in biodegradation

pathways. They add some co-factor or group to make the compound susceptible to

degradation by other enzymes. One such example is benzoate-CoA ligase adds CoA

to benzoate and leads to formation of benzoyl-CoA on which later on reductases,

hydratases and others can act for complete biodegradation. From the UM-BBD we

can see that enzymes belonging to sub classes EC 6.2 and 6.4 play a role in

biodegradation pathways (Table 1.7 – Enzyme sub classes playing role in

bioremediation are in bold).

Table 1.7: Description of ligases.


EC 6.1 Form carbon-oxygen bonds

EC 6.2 Form carbon-sulphur bonds

EC 6.3 Form carbon-nitrogen bonds

EC 6.4 Form carbon-carbon bonds

EC 6.5 Form phosphoric ester bonds

EC 6.6 Form nitrogen-metal bonds

1.6 Elucidating the metabolic pathways involved in biodegradation

Hundreds of xenobiotic compounds, hundreds of enzymes, hundreds of reactions,

hundreds of interlinked pathways and add to this environmental conditions (aerobic or

anaerobic, nutrient availability, moisture and other factors) making biodegradation in

principle a complex process but to keep it simple all reactions have one aim to follow

and that is, mineralization into simple components, whatsoever the compound may be.

Thermodynamic feasibility, chemical equilibrium, reaction dynamism and many such

other factors play a vital role in biodegradation mechanism. Many intermediates can

follow two different pathways for their complete mineralization. The best example is

of catechol which can be degraded by ortho cleavage as well as meta cleavage


35

pathway. These differences do reflect some significance at the genetic level.

Generally genes that encode enzymes of the ortho-cleavage pathways mostly reside

on chromosome/genome, whereas those of meta-cleavage pathways are mostly

harboured by plasmids (Houghton and Shanley, 1994). This also establishes the

importance of other factors playing a role in the mechanism. However, there are

certain factors which do tend to favour a particular pathway. Moreover, compounds

belonging to same class or group of compounds, having similar kind of core structure

or functional groups generally are metabolized to common intermediates and

thereafter the pathway is common. Many a times this common intermediates are

shared even by many pathways. In many cases these intermediates interconvert

depending on reaction dynamism. There are many common intermediates such as

catechol, 4- methylcatechol, protocatechuate, 4- hydroxyl benzoate, maleylacetate, 4-

hydroxyl 2- oxopentanoate, homogentisate, glyoxylate, propanoate, cyanuric acid and

others for aerobic pathways and benzoyl-CoA, acetyl-CoA and others for anaerobic

pathways. Certain compounds undergo aerobic biodegradation, in such cases oxygen

is generally the electron acceptor. However, many contaminated environmental sites

are anoxic and in such cases microorganisms can anaerobically oxidize contaminants

with alternative electron acceptors such as nitrate, sulphate, Fe (III) oxides and others.

In certain bioremediation reactions contaminants are electron acceptors rather than

electron donors, the most prominent example is reductive dechlorination (Lovley,

2003). The other important factor that plays a role in degradation of xenobotic

compounds is that it may be that, initial reactions require anaerobic environment and

final reactions may require aerobic environment. These kinds of need make the

biodegradation tricky for certain compounds. The ultimate necessity is that the

intermediates formed must be able to enter any of the central metabolic pathways

such as glycolysis, Kreb‟s cycle or others so that they are completely mineralized and

does not lead to formation of any dead end metabolites.

The work of Salinero et al. (2009) highlights that there is much to be learned

regarding the metabolic capabilities and life-style of microbial species. Metabolic

analysis of the soil microbe Dechloromonas aromatica strain RCB revealed that it did

not harbour the anticipated (previously characterized) enzymes for anaerobic aromatic


36

degradation. On annotations of genes, it was evident that several metabolic pathways

have yet to be observed experimentally.

An increasing amount and variety of xenobiotic compounds are released in

environment. It is vital to know the fate of these chemical compounds. However, to

find that experimentally without any hint will be a very vague approach. Hence, it will

be useful to predict whether a particular compound is biodegradable and it can be

mineralized by which pathways. UM-BBD besides providing information about

enzymes, compounds, reactions, biotransformation rules and microorganisms; it

includes a Biochemical Periodic Table (UM-BPT) and a rule-based Pathway

Prediction System (UM-PPS) (http://umbbd.msi.umn.edu/predict/) that predicts

plausible pathways for microbial degradation of organic compounds (Gao et al.,

2010). Finley et al. (2009) have described a computational framework (called BNICE)

that can be used for the prediction of novel biodegradation pathways of xenobiotics.

BNICE reproduced the biodegradation routes found experimentally, and in addition, it

expanded the biodegradation reaction networks by generating novel pathways to

degrade xenobiotic compounds that are thermodynamically feasible alternatives to

known biodegradation routes and attractive targets for metabolic engineering.

Metagenomics along with complementary „omics‟ technologies based analyses

harbours the most promising opportunities to delineate the community‟s metabolic

pathways responsible for bioremediation of environmental pollutants.

1.7 Scenario and future perspectives

Bioremediation strategies are multidisciplinary involving ecology, geology,

physiology, genetics, biochemistry, engineering and microbiology. The molecular

biology and „omics‟ technologies have led to tremendous progress in exploration of

polluted sites and understanding the catabolic reactions occurring in bioremediation

processes. The data generated by present „omics‟ technologies are huge and the

foremost need that has arisen is how to store this huge information and above all how

to correlate and integrate them for comprehensive analyses. The use of massively

parallel sequencing technologies is expanding the generated data exponentially.

Therefore, the development of novel and sophisticated bioinformatic tools dedicated


37

to bioremediation studies is necessary for analysis and integration of omic data sets

involving identification of genes, phylogenetic affiliation of sequence reads and

assignment of functions to expressed proteins. This integrated „omics‟ technology

based functional analysis from genes via transcripts to proteins and metabolites at

„meta‟ level will answer all the relevant queries and mysteries that have hampered

bioremediation strategies till now. At present there is a dire need for high-throughput

methods for functional screening and characterization of key enzymes and metabolites

involved in stress response metabolic networks. Developments in existing approaches

and innovations in technologies will lead to novel and extensive approaches.

Conversely, the technologies will have to be economical and applicable at high

throughput level for increased access and applications. In future, analyses by „meta‟

approaches will be successfully used in devising site/contamination specific

bioremediation strategies.

1.8 Objectives of the study

The objectives of the research presented in this thesis are as follows:

Standardization of metagenomic DNA isolation from sites contaminated by

industrial discharges.

Contaminated soil metagenomics - library preparation and functional

screening.

Taxonomic profiling of microbial community inhabiting the contaminated

soil.

Sequence-driven analysis of metagenome for characterization of genes

involved in xenobiotic biodegradation.

Community genomics: Development and characterization of azodye

decolourizing bacterial consortia.

Isolation, characterization and expression analysis of gene coding for

azoreductase.


38

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