while B. subtilis 117S eliminates 117.2 and 351.6 μg/ml nickel through living and
dead biomass, respectively (Abdel-Monem et al. 2010). It was observed that
Ni-resistant B. thuringiensis residing in the soil can withstand up to 10 mM Ni and
eliminates about 82% of it through biosorption from the medium (Das et al. 2014).
Recently, in another strategy, nickel oxide nanoparticles synthesized from
Microbacterium sp. MRS-1 were used for treating nickel electroplating industrial
effluent and removed about 95% nickel (Sathyavathi et al. 2014).
12.8 Discovery of Novel Metal-Resistant Genes Involved
in Bioremediation
Bacteria have developed certain remarkable processes for their existence integrated
into their genome under heavy metal stress. They produce a variety of enzymes and
proteins to get assistance in countering these adverse conditions (Johnsen et al.
2005). Bacteria can be manipulated genetically or metabolically to generate products
which provide unique characters to the host cells. At present, options of novel
adaptation mechanisms in microorganisms for eliminating and ultimately
detoxifying contaminants are being explored by workers. Genes which code for
the enzymes that can alter the oxidation state of heavy metal from more harmful to
less harmful forms can be introduced like for coding bacterial merA and mercuric
reductase incorporation into other bacteria to improve bioremediation (Dash and Das
2015). Therefore, altered genes can be incorporated which assists in acquiring new
detoxification methods for heavy metals (Arora et al. 2010). One more strategy
which can be employed is the utilization of in silico procedures. Currently, due to the
introduction of computers and software, it is now feasible to obtain knowledge on
any subject from a single source. Several databases are available which give
knowledge about the harmful effects of compounds as well as their location, features
and degradation pathways. Certain significant databases are USEPA (http://www.
epa.gov/), ATSDR (http://www.atsdr.cdc.gov/) and KEGG PATHWAY Database
(http://www.genome.jp/). Khan et al. (2013) have compared the utilization of these
computational sources to perform bioremediation virtually prior to test it on the
location. Eleven software tools were listed by them to forecast the harmful effects of
compounds. Ten databases containing knowledge about harmful effects of several
compounds were also explained by them. Additionally, 15 programs were also
employed to test the environmental degradability of compounds. Biodegradability
Evaluation and Simulation System (BESS) and Biochemical Network Integrated
Computational Explorer (BNICE) are two of the highly successful programs. Nowadays, due to this information with the assistance of biological pathway prediction
software like Scansite 2.0 (Obenauer et al. 2003), BioCyc (Karp et al. 2005),
SMART5 (Letunic et al. 2006), STRING7 (Von Mering et al. 2007) and KEGG,
exhaustive studies on interactions of different biomolecules in silico are possible,
and new proteins and genes can also be deduced which may be utilized in bioremediation (Kanehisa and Goto 2000; Das et al. 2016).
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N. Srivastava
dead biomass, respectively (Abdel-Monem et al. 2010). It was observed that
Ni-resistant B. thuringiensis residing in the soil can withstand up to 10 mM Ni and
eliminates about 82% of it through biosorption from the medium (Das et al. 2014).
Recently, in another strategy, nickel oxide nanoparticles synthesized from
Microbacterium sp. MRS-1 were used for treating nickel electroplating industrial
effluent and removed about 95% nickel (Sathyavathi et al. 2014).
12.8 Discovery of Novel Metal-Resistant Genes Involved
in Bioremediation
Bacteria have developed certain remarkable processes for their existence integrated
into their genome under heavy metal stress. They produce a variety of enzymes and
proteins to get assistance in countering these adverse conditions (Johnsen et al.
2005). Bacteria can be manipulated genetically or metabolically to generate products
which provide unique characters to the host cells. At present, options of novel
adaptation mechanisms in microorganisms for eliminating and ultimately
detoxifying contaminants are being explored by workers. Genes which code for
the enzymes that can alter the oxidation state of heavy metal from more harmful to
less harmful forms can be introduced like for coding bacterial merA and mercuric
reductase incorporation into other bacteria to improve bioremediation (Dash and Das
2015). Therefore, altered genes can be incorporated which assists in acquiring new
detoxification methods for heavy metals (Arora et al. 2010). One more strategy
which can be employed is the utilization of in silico procedures. Currently, due to the
introduction of computers and software, it is now feasible to obtain knowledge on
any subject from a single source. Several databases are available which give
knowledge about the harmful effects of compounds as well as their location, features
and degradation pathways. Certain significant databases are USEPA (http://www.
epa.gov/), ATSDR (http://www.atsdr.cdc.gov/) and KEGG PATHWAY Database
(http://www.genome.jp/). Khan et al. (2013) have compared the utilization of these
computational sources to perform bioremediation virtually prior to test it on the
location. Eleven software tools were listed by them to forecast the harmful effects of
compounds. Ten databases containing knowledge about harmful effects of several
compounds were also explained by them. Additionally, 15 programs were also
employed to test the environmental degradability of compounds. Biodegradability
Evaluation and Simulation System (BESS) and Biochemical Network Integrated
Computational Explorer (BNICE) are two of the highly successful programs. Nowadays, due to this information with the assistance of biological pathway prediction
software like Scansite 2.0 (Obenauer et al. 2003), BioCyc (Karp et al. 2005),
SMART5 (Letunic et al. 2006), STRING7 (Von Mering et al. 2007) and KEGG,
exhaustive studies on interactions of different biomolecules in silico are possible,
and new proteins and genes can also be deduced which may be utilized in bioremediation (Kanehisa and Goto 2000; Das et al. 2016).
342
N. Srivastava