Nova Biotechnol Chim (2022) 21(1): e1083
DOI: 10.36547/nbc.1083
1
Nova Biotechnologica et Chimica
Isolation and statistical optimization of rhodanese (a thiosulphate sulphur
transferase) production potential of Klebsiella oxytoca JCM 1665 using
response surface methodology
Babamotemi Oluwasola Itakorode1,2,, Raphael Emuebie Okonji2
1Department of Chemical Sciences, Oduduwa University Ipetumodu, Ile-Ife, Osun State, Nigeria
2Department of Biochemistry and Molecular Biology, Faculty of Science, Obafemi Awolowo University Ile-Ife, Osun State,
Nigeria
Corresponding author: itakorgsoli29@gmail.com
Article info
Article history:
Received: 2nd August 2021
Accepted: 20th December 2021
Keywords:
Klebsiella oxytoca
Optimization
Polymerase chain reaction
Response surface methodology
Rhodanese
4
Abstract
Microorganisms are increasingly being used in cyanide bioremediation. Several
organisms have been reported to thrive in cyanide contaminated wastewater due to
their ability to produce cyanide detoxifying enzymes. However, to improve the
production efficiency of these enzymes combinations of process variables need to be
optimized. In this study, Klebsiella oxytoca JCM 1665 was isolated from industrial
wastewater, identified by sequencing its 16S rRNA gene and subjected to rhodanese
production using submerged fermentation. The conditions for production were
optimized using response surface methodology (RSM). Central composite design
was employed to evaluate the effects of three production parameters – peptone (1 –
5 %), KCN (0.1 – 0.5 %), and time of incubation (1 – 24 h). Second-order
polynomial model was used to predict the response. Rhodanese activity in the
experiments varied from 0.05 to 7.5 RU.mg-1. Under the optimum conditions of
4.35 % peptone, 0.4 % KCN and incubation time of 13 hr., the value for rhodanese
yield was 7.810 U.mL-1. The R2 value for the model was 0.9925 (R2 = 0.9925).
Also, the experimental values are in accordance with those predicted, indicating the
suitability of the employed model and the success of RSM in optimizing the
production conditions.
Introduction
Cyanides are toxic chemicals that are widely
present in both the abiotic and biotic components of
an ecosystem. It acts as a defense mechanism in
various organisms such as fungi, bacteria, algae
and plants. However, the amount of cyanide
generated by these organisms is insignificant when
compared to the ones generated through
anthropogenic activities (Luque-Almagro et al.
2016). In industries, cyanide is used for gold
extraction and in the synthesis of various
agrochemicals such as fertilizers, liming and
pesticides. Cyanide toxicity is due to its high
binding affinity to metal ions; it inhibits
metalloenzymes especially the cytochrome c
oxidase in the electron transport chain (Luque-
Almagro et al. 2016). The amount of cyanide in
the environment has been observed to have
increased greatly due to rapid industrialization and
cyanide leaching methods have been the major
techniques in gold extraction. The demand for gold
jewellery in countries like the United States, Russia
and most African countries has led to the increase
mailto:itakorgsoli29@gmail.com
Nova Biotechnol Chim (2022) 21(1): e1083
2
in mining activities (Mudder et al. 2000). Industrial
processes such as mining and electroplating have
contributed significantly to the cyanide
contamination in water bodies. To minimize
environmental disasters, cyanide-containing liquid
must be properly treated before releasing into the
water bodies. The removal of cyanide from the
effluent is one of the challenges in the cyanide-
based industries to fulfil their standards and
recycling of the water (Kuyucak and Akcil 2013).
Despite the toxicity of cyanide, cyanotrophic
microorganisms such as Pseudomonas sp. (Akcil
and Mudder 2003; Oyedeji et al. 2013), Bacillus
pumilus (Kandasamy et al. 2015), and Bacillus
cereus (Itakorode et al. 2019) has been reported to
survive in the presence of cyanide due to their
ability to synthesize cyanide metabolizing enzyme
such as rhodanese.
Rhodanese catalyzes cyanide detoxification by
transferring sulfur from a suitable substrate such as
thiosulfate to cyanide. This leads to the formation
of a less toxic compound (thiocyanate) that can be
excreted in the urine (Eq. 1):
S2O3
2- + CN- SCN- + SO3
2- (1)
where S2O3
2- – thiosulfate, CN- – cyanide ion,
SCN- – thiocyanate ion, SO3
2- – sulfite.
Its role as a detoxification enzyme is supported by
its mitochondria and cytosolic localization
(Cipollone et al. 2007; Steiner et al. 2018).
Although the use of microorganisms has been
proven to be viable in cyanide bioremediation,
enzymatic proteins may be of good alternative
(Gianfreda et al. 2004; 2010). Enzymes can retain
its activity at extreme conditions that limit
microbial activities. Also, they are not inhibited by
inhibitors of microbial metabolism and the
technique is eco-friendly. Therefore, the
importance of producing cyanide metabolizing
enzymes for industrial application cannot be
overemphasized.
Many factors including incubation time, the
temperature, the pH, carbon and nitrogen sources
contribute to the efficacy of metabolite production
by organisms (Adekunle et al. 2017; Itakorode et
al. 2019). Optimization of metabolites production
may be achieved by either one factor at a time or
statistical means (Rodrigues et al. 2008;
Annegowda et al. 2012). Response surface
methodology (RSM) is a statistical experimental
protocol used in mathematical modelling (Triveni
et al. 2001; Gong et al. 2012). RSM technique
reduces the experimental assays and improves the
statistical interpretation and interaction between
variables (Tsapatsaris et al. 2004; Yim et al. 2012).
The RSM can give a mathematical equation. It is
also helpful to calculate the response value when
different levels of variables are set. Central
Composite Design is a widely used protocol in
response surface methodology (Yang et al. 2008;
Rao 2010). The impact of industrialization on the
quality of the environment is evident and there is a
need to have eco-friendly strategies to treat effluent
for the sustainable development of industries.
Hence, this study aims to investigate optimal
production conditions of rhodanese by Klebsiella
oxytoca.
Experimental
Wastewater was collected from iron and steel
smelting company in sterile plastic bottles and
stored at 4 oC. It was centrifuged at 5,000 × g for
10 min. The clarified supernatant was collected and
used for further analysis. Physicochemical
parameters such as pH, turbidity, chemical oxygen
demand, total dissolved solids, dissolved oxygen,
cadmium and Lead were determined using the
methods described by Ademoroti (1996).
Isolation and screening for rhodanese production
A loopful of diluted sample was spread on
a modified Bushnell Hass agar plate and incubated
at 37 °C for 96 h to select for cyanide degrading
bacteria. Rhodanese producing potential of the
isolates was done by employed the method
described by Zlosnik and Williams (2004).
Production medium consists of NaCl (0.5 g/100
mL), bacteriological peptone (1 g/100 mL) and
KCN (0.3 g/100 mL) at pH of 6.0. The growth
media prepared were inoculated with standardized
cells suspension. The culture media were checked
for rhodanese activity for 24 h at an interval of
2 h.
Rhodanese
Nova Biotechnol Chim (2022) 21(1): e1083
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Isolates identification and characterization
The most productive strain was identified by
sequencing its 16S rRNA gene. This was carried
out at the Bioscience Centre of the International
Institute of Tropical Agriculture (Ibadan, Oyo
State, Nigeria). DNA extraction was carried out
using modified method of Trindade et al. (2007).
The assay mixture for Polymerase Chain Reaction
(PCR) amplification consists of 4 µL of the DNA
solution, 0.4 µL of 10 mM dNTPs, 2 µL of 25 mM
MgCl2, 1 µL of 10 pmol each of primer (Forward
5’-CCAGCAGCCGCGGTAATACG-3’ and
Reverse 5’-TCGGCTACCTTGTTACGACTTC-3’)
(Yamamoto and Harayama 1995), 0.24 µL of Taq
polymerase (1 U.µL-1) (Promega USA) and the 5
µL of 5× PCR buffer. Sterile DNase free water was
added to make a volume of 25 µL. PCR was
conducted in an automated thermal cycler. The
thermal conditions were as follows: denaturation at
94 oC for 1 min, annealing at 56 oC for 1 min and 1
min extension at 72 oC. The PCR amplicons were
visualized using 1.5 % agarose gel electrophoresis.
Enzyme assay and experimental design
Sodium thiosulfate (Na2S2O3) and potassium
cyanide (KCN) were used as substrates for
rhodanese activity. 1 mL of the assay mixture
consists of 50 mM borate buffer (pH 9.4), 0.25 M
KCN, and Na2S2O3 and 0.1 mL of the enzyme. The
enzyme reaction was terminated with 0.5 mL 38 %
formaldehyde after 1 min of incubation at 25 oC
(Lee et al. 1995). Concentration of thiocyanate
produced was quantified by the addition of 1.5 mL
Sorbo reagent (which is made up of 10 g Fe
(NO3)2·9H2O, 20 mL HNO3 and 80 mL distilled
water) (Sorbo 1953). The absorbance of the
reaction medium was taken at 460 nm. The unit of
rhodanese activity (RU) is defined as the
micromoles of the product (thiocyanate) formed in
one minute. The protein concentration was
determined by the method of Bradford (1976),
bovine serum albumin (BSA) was used as standard.
The extracellular production of rhodanese was
established by RSM which was employed to
determine the best combination of variables for
optimum rhodanese production by K. oxytoca. The
independent variables used in this study were
peptone concentration (A – % (w/v)), potassium
cyanide (B – % (w/v)), and time (C – h) while
response was rhodanese activity (RU.mg-1).
The coded and uncoded levels of the independent
variables are presented in Table 1.
Table 1. Coded and decoded levels of independent variables
used in the RSM design.
Run A: Peptone B: KCN C: Time
1 -1 (0.36) 0 (0.30) 0 (12.50)
2 -1 (1.00) +1 (0.50) -1 (1.00)
3 +1 (6.30) 0 (0.30) 0 (12.50)
4 0 (3.00) 0 (0.30) 0 (12.50)
5 -1 (1.00) -1 (0.10) +1 (24.00)
6 +1 (5.00) +1 (0.50) -1 (1.00)
7 0 (3.00) 0 (0.30) 0 (12.50)
8 -1 (1.00) +1 (0.50) +1 (24.00)
9 +1 (5.00) -1 (0.10) +1 (24.00)
10 0 (3.00) -1 (0.03) 0 (12.50)
11 0 (3.00) 0 (0.30) +1 (31.84)
12 0 (3.00) 0 (0.30) 0 (12.50)
13 0 (3.00) +1 (0.60) 0 (12.50)
14 0 (3.00) 0 (0.30) 0 (12.50)
15 0 (3.00) 0 (0.30) 0 (12.50)
16 +1 (5.00) +1 (0.50) +1 (24.00)
17 0 (3.00) 0 (0.30) -1 (6.84)
18 +1 (5.00) -1 (0.10) -1 (1.00)
19 0 (3.00) 0 (0.30) 0 (12.50)
20 -1 (1.00) -1 (0.10) -1 (1.00)
Statistical analysis
The experimental data were analyzed using JMP
11’s response surface regression algorithm
(statistical analysis system Inc., SAS). P-values
under 0.05 (P < 0.05) were considered significant.
ANOVA was used to assess the quality of the
mathematical models fitted by RSM, based on the
F-test and the percentage of total explained
variance (R), as well as the adjusted determination
coefficient (R2adj), which provide a measurement
of how much of the variability in the observed
response values could be explained by the
experimental factors and their linear and quadratic
interactions (Granato et al. 2012). To fit the data, a
second-order polynomial quadratic equation (Eq. 2)
was used.
(2)
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where Y is the predicted response, β0, βi, βii, βij,
are the correlation coefficients for intercept, linear,
quadratic and interaction terms, respectively and xi
and xj are the levels of the independent variables.
Experimental data were fitted to the chosen
regression model to have a better understanding of
the relationship between each variable and
response. The predictive equation of RSM was
used to find the optimal conditions for the
production of rhodanese. The validity of the model
was determined by comparing the experimental and
predicted response values.
Results
Isolation and identification of the bacteria isolate
Table 2 shows the physicochemical properties of
the wastewater such as the chemical oxygen
demand, pH, turbidity, total dissolved solids,
dissolved oxygen, lead, and cadmium. The
selection of microorganisms from the wastewater
led to the isolation of nine gram-positive and two-
gram negative bacteria. One strain was chosen for
further study due to its appreciable rhodanese
production. Molecular analysis based on 16S rRNA
gene sequencing revealed that isolates JCM 1665
shared 99.65 % homology with K. oxytoca
AY873801, 99.72 % homology with K. oxytoca
MF144436 and 99.79 % homology with K. oxytoca
MT568561 strain (Table 3).
Table 2. Physiochemical analysis of the wastewater.
Parameter
pH 6.0 ± 1
Concentration of
parameter [mg.L-1]
Turbidity 8.23 ± 2.74
Chemical Oxygen Demand 74.38 ± 5.19
Total dissolved solids 536 ± 10.6
Lead 0.02 ± 0.002
Cyanide 67.49 ± 9.2
Cadmium 0.024 ± 0.006
Dissolved Oxygen 3.94 ± 0.88
Table 3. Sequence identity of K. oxytoca (MN590525) with
other Klebsiella oxytoca.
Klebsiella
oxytoca
%
Identity
Accession in the
GenBank database
MT568561 99.79 MT568561
MF144436 99.72 MF144436
AY873801 99.65 AY873801
The sequence was deposited in the GenBank
database of the NCBI as accession MN590525
(https://www.ncbi.nlm.nih.gov/nucleotide/).
Analysis of the model
The result for the analysis of the model for the
production optimization is shown in Table 4. In this
model, two linear (B and C) and five quadratic
models (AB, AC, A2, B2, and C2) were found to be
significant at the level of P < 0.05.
Table 4. Experimental and predicted result for rhodanese
activity.
Run Response
(activity U.mg-1)
Actual
value
Predicted
value
1 5.00 5.00 5.30
2 2.50 2.50 2.13
3 6.00 6.00 5.69
4 7.10 7.10 7.25
5 5.70 5.70 5.50
6 5.50 5.50 5.71
7 7.20 7.20 7.25
8 5.70 5.70 5.71
9 2.00 2.00 2.38
10 3.50 3.50 3.35
11 3.50 3.50 3.43
12 7.10 7.10 7.25
13 6.50 6.50 6.64
14 7.50 7.50 7.25
15 7.20 7.20 7.25
16 6.00 6.00 5.94
17 0.05 0.05 0.10
18 2.00 2.00 2.00
19 7.40 7.40 7.25
20 1.70 1.70 1.77
The result for the fitting quadratic model is listed in
Table 5. The result of the analysis of variance
(ANOVA) indicates that the model was significant
(P < 0.05) for the response of the dependent
variables (rhodanese activity). The result also
indicates a good model performance with
correlation coefficient (R2) values of 0.9925. The
fitted quadratic model for rhodanese production is
shown in Eq. 3.
4
https://www.ncbi.nlm.nih.gov/nucleotide/
Nova Biotechnol Chim (2022) 21(1): e1083
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Table 5. Analysis of Variance (ANOVA) for Response
Surface Quadratic Model for the production of Rhodanese.
Source SS df MS F-value p-value
Model 99.35 9 11.04 147.52 < 0.0001*
A-peptone 0.1832 1 0.1832 2.45 0.1487
B-KCN 13.04 1 13.04 174.28 < 0.0001*
C-Time 13.35 1 13.35 178.40 < 0.0001*
AB 5.61 1 5.61 74.99 < 0.0001*
AC 5.61 1 5.61 74.99 < 0.0001*
BC 0.0112 1 0.0112 0.1503 0.7063
A² 5.56 1 5.56 74.36 < 0.0001*
B² 9.18 1 9.18 122.69 < 0.0001*
C² 54.15 1 54.15 723.62 < 0.0001*
SS – sum of squares; df – degrees of freedom; MS – mean
square. R2 = 0.9925; R2 adj = 0.9858; CV = 5.52 %. (*values
statistically significant at P < 0.05).
Analysis of response surface
The best way of expressing the relationship
between the independent variables and dependent
variables is to graphically plot the response surface
plots generated by the model (Fig. 1, 2, 3). Fig. 1
showed the interaction between potassium cyanide
(KCN) and incubation time while holding peptone
at the center point (0). Fig. 2 showed the interaction
between potassium cyanide (KCN) and peptone
while incubation time is held constant. Fig. 3
showed the interaction between incubation time
and peptone while KCN is held constant.
Fig. 1. Response surface plots showing the effects of KCN (%
w/v) and peptone (% w/v) on rhodanese production.
Fig. 2. Response surface plots showing the effects of
incubation time (hr) and peptone (% w/v) on rhodanese
production.
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Fig. 3. Response surface plots showing the effects of
incubation time (hr) and KCN (% w/v) on rhodanese
production.
Discussion
Klebsiella oxytoca was isolated from an industrial
effluent, identified and subjected to extracellular
rhodanese production. Rhodanese production
ability has been identified in a variety of bacteria
species such as E. coli (Ray et al. 2000),
P. aeruginosa (Cipollone et al. 2008), Azotobacter
vinelandii (Kaewkannetra et al. 2009), Bacillus
brevis (Oyedeji et al. 2013) and Bacillus cereus
(Itakorode et al. 2019). Enzyme production by an
organism is often dependent on the growth of the
bacterium in the appropriate media composition.
The optimization of culture media and
environmental conditions is essential for effective
production as it tends to reduce cost of production
by increasing yield (George-Okafor et al. 2012).
The optimum nutritional requirement for rhodanese
production was determined to obtain maximum
enzyme production by K. oxytoca. A central
composite design (20 runs) was chosen with start
and center points. The design was rotatable; this
means that the designs have points that are
equidistant from the center. Experiments at the
center were carried out to obtain an estimation of
the experimental error. The designed experiment
matrix and the experimental results are presented in
Tables 1 and 4. The rhodanese yield varied (from
0.05 to 7.5 RU.mg-1). The rhodanese production
yield reached its maximum value (7.5 RU.mg-1) at
3 % of peptone, 0.3 % of KCN and incubation time
of 12.5 h (run 17). As noted, rhodanese producing
ability of K. oxytoca was affected by the time of
incubation. The analysis of the overall data set
indicated that incubation time, KCN and interaction
AC had the most pronounced effects on the
response (Table 5). Also, from Eq. 3, it is evidence
that KCN (B) concentration and incubation time
(C) had the highest positive effect on the rhodanese
production while interaction (AC) and (C2) had the
highest negative effect on production.
Analysis of variance (ANOVA) was important in
determining the adequacy and significance of the
quadratic model. ANOVA summary is shown in
Table 5. The fitness of the model was expressed by
the R2 value, which is 0.9925, indicating that 99.25
% of the variability in the response can be
explained by the model. The adjusted R2 value of
0.9858 suggested that the model was significant. A
very low value of coefficient of the variation (CV)
5.52 % indicated a very high degree of precision
and a good deal of reliability of the experimental
values.
The interaction between KCN and peptone
concentration at a constant time of incubation is
shown in Fig. 1. The result showed that rhodanese
production increases as the concentration of both
variables increases. At high concentrations, a
gradual fall in production was observed. Research
had shown KCN and peptone to influence the
production of cyanide detoxifying enzymes
(Adekunle et al. 2017). Itakorode et al. (2019)
reported KCN as the best carbon source for B.
cereus. The ability of P. putida and B. pumilus to
make use of cyanide as carbon source was also
reported by Kandasamy et al. (2015). Okonji et al.
(2018) also reported the ability of Pseudomonas
straminea to make use of peptone as nitrogen
source. The high cyanide tolerance of K. oxytoca
observed was probably due to its ability to
synthesize rhodanese as a means of surviving in
cyanide contaminated environment. Fig. 2 showed
the interaction effect of peptone concentration and
incubation time while keeping KCN concentration
constant. In this interaction, minimal rhodanese
production was observed at a low concentration of
peptone. However, as concentration and incubation
6
Nova Biotechnol Chim (2022) 21(1): e1083
3
time increases, an increase in rhodanese activity
was recorded. The same trend was also observed in
the interaction between incubation time and KCN.
However, a decline in production was noted at high
time of incubation. The decline in rhodanese
production may be due to exhaustion of the
nutrients or accumulation of other products or
metabolites which are both inhibitory to the growth
of the bacterium and rhodanese production. This
result agrees with the findings of Okonji et al.
(2018) who observed a decrease in metabolite
production after 15 hours of incubation. Linawati et
al. (2002) also reported the influence of
environmental conditions on the production of
pigment by Serratia marcescens.
The optimal value of the independent variables for
rhodanese production was examined using the
maximum desirability. The result of optimal
conditions used to obtain the highest rhodanese
production by K. oxytoca were 4.35 % peptone, 0.4
% KCN and incubation time of 13 hours, at which
rhodanese yield was 7.810 RU.mg-1.
Conclusion
It can be concluded that the regression equations
obtained in this study can be used to maximize the
rhodanese production by K. oxytoca. Also, further
study is required to improve the production of the
enzyme through genetic engineering and to
examine the cyanide bioremediation potential of
the rhodanese synthesized by the organism.
Conflict of interest
The authors declare that they have no conflict of interest.
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