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Contents lists available at ScienceDirect
International Journal of Biological Macromolecules journal homepage: www.elsevier.com/locate/ijbiomac
Production of polysaccharide-based bioflocculant for the synthesis of silver nanoparticles by Streptomyces sp
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Panchanathan Manivasagan a , Kyong-Hwa Kang a , Dong Gyu Kim b , Se-Kwon Kim a,c,∗ a
Marine Bioprocess Research Center, Pukyong National University, Busan 608-739, Republic of Korea Specialized Graduate School Science & Technology Convergence, Pukyong National University, Busan 608-739, Republic of Korea Specialized Graduate School Science & Technology Convergence, Department of Marine-Bio, Convergence Science, Pukyong National University, Busan 608-739, Republic of Korea b c
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a b s t r a c t
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Article history: Received 9 February 2015 Received in revised form 4 March 2015 Accepted 15 March 2015 Available online xxx
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Keywords: Bioflocculants Polysaccharides Silver nanoparticles Response surface methodology Streptomyces sp.
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1. Introduction
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25Q2 26 27 28 29 30 31 32 33 34 35 36
Polysaccharide-based bioflocculants have attracted considerable attention in recent years due to their biodegradable, harmless and negligible secondary pollution. Bioflocculants are organic macromolecular substances secreted by microorganisms. A simple, cost-effective and green method was developed for the biosynthesis of silver nanoparticles using polysaccharides as reducing and stabilizing agents. In this paper, we report on the production and optimization of polysaccharide-based bioflocculant for the green synthesis of silver nanoparticles by Streptomyces sp. MBRC-91. Medium composition and culture conditions for polysaccharide-based bioflocculants were statistically optimized by response surface methodology (RSM). The bioflocculant production was statistically optimized with most significant factors, namely palm jaggery (18.73 g/L), yeast extract (2.07 g/L), K2 HPO4 (3.74 g/L) and NaCl (0.38 g/L), respectively. The biosynthesized silver nanoparticles were characterized by UV–vis spectroscopy, XRD, FTIR, FESEM, EDXA and HRTEM. The biosynthesized silver nanoparticles revealed strong antibacterial activity in sewage water and this result could make a new avenue in the wastewater treatment. Therefore, the biosynthesized silver nanoparticles can be extended as an alternative for the development of new bactericidal bionanomaterials for wastewater treatment and biotechnological applications. © 2015 Published by Elsevier B.V.
Marine microbial-produced bioflocculants have received considerable scientific and biotechnological attention in recent years due to their biodegradability, non-toxic and their degradation intermediates are not secondary pollutants [1,2]. Bioflocculants are marine microorganism-produced natural organic macromolecule substances that can flocculate suspended solids, cells, colloidal solids, etc. To date, many bioflocculant-producing marine microorganisms have been reported, including bacteria [3], actinobacteria [4], algae [5], fungi [6] and yeast [7]. Bioflocculants are mainly composed of biological macromolecules such as polysaccharide, protein, glycoprotein, nucleic acid, etc. [8]. Among these biological macromolecules, the polysaccharide-based bioflocculants received
∗ Corresponding author at: Marine Bioprocess Research Center, Pukyong National University, Busan 608-739, Republic of Korea. Tel.: +82 51 629 6870; fax: +82 51 629 6865. E-mail addresses: [email protected] (P. Manivasagan), [email protected] (S.-K. Kim).
considerable attention in recent years due to their high efficiency in disposing of various kinds of pollutants, including dyeing pigment, heavy metal ion and other suspended pollutants [9–11]. Bioflocculants have been widely used in industrial processes, including wastewater treatment, downstream processing, food and fermentation process [12]. The marine environment is not yet fully explored, it is a huge treasure trove of marine actinobacteria resources [13,14]. Nowadays, marine actinobacteria are the best sources of secondary metabolites with various biological activities. Many representatives of the order Actinomycetales are prolific producers of thousands of biologically active secondary metabolites [15]. Among the actinobacteria, streptomycetes group is considered most economically important because, out of the approximately more than 11,900 known antibiotics, 50–55% is produced by Streptomyces [16]. Marine actinobacteria, particularly, streptomycetes have attracted much attention because of their ability to make secondary metabolites such as antibiotics, immunosuppressors, and many other bioactive compounds [17]. Exploitation of Streptomyces species in marine bio-nanotechnology has also recently received considerable attention, owing to their various applications [18]. Marine
http://dx.doi.org/10.1016/j.ijbiomac.2015.03.022 0141-8130/© 2015 Published by Elsevier B.V.
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actinobacterial synthesis of metal nanoparticles has good potential to develop simple, cost-effective and eco-friendly methods for production of important bionanomaterials [19,20]. Silver nanoparticles are widely investigated owing to their broad range of application as antibacterial, catalyst and as a biosensor [21]. Polysaccharides are the polymers of monosaccharides. In nature, polysaccharides have various resources from algae, plants, animals and microorganisms were used as reducing and stabilizing agents for the biosynthesis of metal nanoparticles [22]. Polysaccharides are highly stable, safe, non-toxic, hydrophilic and biodegradable. Polysaccharide-based bioflocculants can be used for high-performance nanomaterials production, since they easily form a variety of liquid crystals in aqueous solutions and bioflocculant-mediated processes are highly profitable. The production of polysaccharide-based bioflocculants is not species specific and each strain of same species produces different kinds of polysaccharides with different biological applications [23]. Therefore, this study involves the production and optimization of polysaccharide-based bioflocculant for the green synthesis of silver nanoparticles using Streptomyces sp. MBRC-91. To the best of our knowledge, this marine actinobacterium Streptomyces sp. MBRC91 has never been used for the production of polysaccharide-based bioflocculant for the green synthesis of silver nanoparticles.
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2. Materials and methods
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and incubated at 30 ◦ C with shaking at 180 rpm for 96 h. Samples were withdrawn at different time intervals and monitored for cell growth and flocculating activity. Culture broth was centrifuged at 10,000 rpm for 20 min to separate the cells which were washed twice with distilled water and dried at 65 ◦ C to constant weight as a measurement of cell growth. The culture supernatant was used to study flocculating activity [24]. All experiments were performed in triplicates for the mean calculation.
2.4. Determination of flocculating activity Flocculating activity was determined using the previous method with a slight modification [8]. Kaolin clay was chosen as the suspended solid to calculate the flocculating activity. Kaolin clay was chosen as the suspended solid to calculate the flocculating activity. First, 93 mL kaolin suspension (4.0 g/L), 5.0 mL of a 1% CaCl2 (w/v) and 2.0 mL of a culture supernatant were mixed and stirred at 4 min and then allowed to incubate for 5 min. After incubation, the supernatant absorbance was measured by a spectrophotometer at 550 nm (Shimadzu No. UV-1800, Japan). The culture supernatant was replaced with a culture medium at the same concentration in the control experiment. The flocculating activity was calculated according to equation. flocculating activity (%) =
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2.1. Chemicals Silver nitrate was purchased from Sigma-Aldrich (St. Louis, USA). All media components were purchased from Lab M Limited (Bury, UK). All chemicals were of analytical grade and procured from Sigma-Aldrich (St. Louis, USA). 2.2. Isolation and molecular identification of marine actinobacteria
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A total of 20 marine actinobacterial strains were isolated from the marine sediment samples collected from the Busan coast (Latitude 35◦ 09 N; Longitude 129◦ 07 E), South Korea. All the strains were grown on starch casein agar medium and maintained as spore suspension. The composition of the bioflocculant screening medium was as follows: glucose, 10 g/L; yeast extract, 0.5 g/L; KH2 PO4 , 2 g/L; K2 HPO4 , 5 g/L; (NH4 )2 SO4 , 0.2 g/L; urea, 0.5 g/L and NaCl, 0.5 g/L with the initial pH 7.0. Each isolated strain was inoculated in 250 mL Erlenmeyer flasks containing 50 mL of screening medium and incubated at 30 ◦ C in a rotary shaker at 180 rpm for 96 h. After the incubation period, the culture supernatants were determined for flocculating activity. The strain MBRC-91 with the highest flocculating activity was selected for further investigation. The isolate MBRC-91 was identified based on 16S rDNA sequencing. Its isolated genomic DNA was subjected to PCR amplification of 16S rDNA using universal primers. Amplified product was gel purified using QIAquick gel extraction kit and purified products were sequenced. The 16S rDNA sequence (∼1493 bp) was compared with currently available actinobacteria sequences in GenBank. The GenBank accession numbers of strain MBRC-91 is KC179813.
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2.3. Production of bioflocculant
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Production of bioflocculant by Streptomyces sp. MBRC-91 was performed in 500 mL Erlenmeyer flasks containing 100 mL of the production medium with 180 rpm at 30 ◦ C. The composition of the modified production medium was as follows: palm jaggery, 20 g/L; yeast extract, 2.5 g/L; NH4 NO2 , 1.0 g/L; MgCl2 , 0.2 g/L; K2 HPO4 , 5 g/L and NaCl, 0.5 g/L. The initial pH value of the medium was adjusted to 7.0. Each flask was inoculated with 4% (v/v) of the seed culture
(A − B) × 100 A
where A and B were OD550 of the control and of the sample supernatant, respectively.
2.5. Statistical optimization of bioflocculant production using response surface methodology Plackett–Burman design is a powerful tool used to screen n variables in only n + 1 experiment and this design significantly decreases the number of experiments needed to efficiently achieve experimental goals. In the present study, 6 independent medium components were investigated using Plackett–Burman design to identify the components that significantly affected bioflocculant production. The components included palm jaggery, yeast extract, NH4 NO2 , MgCl2 , K2 HPO4 , and NaCl. The design was developed by the MINITAB® software (Minitab package version 16.0 Inc., USA). All the variables were evaluated in twelve experimental trials and the average bioflocculant yield for each trial was used as the response variable. Regression analysis determined the variables that had a significant (95% level (p < 0.05)) effect on bioflocculant yield, and these variables were evaluated in further optimization experiments. After the critical medium components were identified by PB design, RSM was employed to optimize the component concentration to improve bioflocculant production. Six variables were selected from Plackett–Burman design that significantly affected bioflocculant production was further optimized by response surface methodology (RSM). A Box–Behnken design of the experiments was formulated to investigate four flocculation parameters. Each 250 mL culture of Streptomyces sp. MBRC-91 was added in 500 mL Erlenmeyer flasks and incubated at 30 ◦ C in a rotary shaker at 180 rpm for 96 h. The parameters were set according to the orthogonal values of Box–Behnken design (Table 1). The experiments were performed in triplicate. RSM is known to evaluate the interaction between the significant factors of an experiment and optimize them. Four-level factor experiment setup was designed using MINITAB® software (Minitab package version 16.0 Inc., USA) and the quality of analysis model was based on an analysis of variance (ANOVA). The response variable (Y), representing the bioflocculation activity, was fitted
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Table 1 Experimental design and results of Box–Behnken optimization experiment.
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Runs
X1
X2
X3
X4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
−1 (10) +1 (20) −1 (10) +1 (20) 0 (15) 0 (15) 0 (15) 0 (15) −1 (10) +1 (20) −1 (10) +1 (20) 0 (15) 0 (15) 0 (15) 0 (15) −1 (10) +1 (20) −1 (10) +1 (20) 0 (15) 0 (15) 0 (15) 0 (15) 0 (15) 0 (15) 0 (15)
−1 (0.5) −1 (0.5) +1 (2.5) +1 (2.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) −1 (0.5) +1 (2.5) −1 (0.5) +1 (2.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) −1 (0.5) +1 (2.5) −1 (0.5) +1 (2.5) 0 (1.5) 0 (1.5) 0 (1.5)
0 (3) 0 (3) 0 (3) 0 (3) −1 (1) +1 (5) −1 (1) +1 (5) 0 (3) 0 (3) 0 (3) 0 (3) −1 (1) −1 (1) +1 (5) +1 (5) −1 (1) −1 (1) +1 (5) +1 (5) 0 (3) 0 (3) 0 (3) 0 (3) 0 (3) 0 (3) 0 (3)
0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) −1 (0.1) −1 (0.1) +1 (0.5) +1 (0.5) −1 (0.1) −1 (0.1) +1 (0.5) +1 (0.5) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) −1 (0.1) −1 (0.1) +1 (0.5) +1 (0.5) 0 (0.3) 0 (0.3) 0 (0.3)
using a second-order polynomial equation given as Y=
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ˇ0 + ˇ1 X1 + ˇ2 X2 + ˇ3 X3 + ˇ4 X4 + ˇ11 X12
+ ˇ22 X22
+ ˇ33 X32
+ˇ44 X42 + ˇ12 X1 X2 +ˇ13 X1 X3 + ˇ14 X1 X4 + ˇ23 X2 X3 + ˇ24 X2 X4 + ˇ34 X3 X4
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where Y is the predicted response, ˇ0 the constant, X1 –X4 the input variables, ˇ1 –ˇ4 the linear coefficient, ˇ11 –ˇ44 the quadratic coefficients and ˇ12 –ˇ44 are the second-order interactive coefficients. The actual value of coded levels of different parameters which are palm jaggery (X1 ), yeast extract (X2 ), K2 HPO4 (X3 ) and NaCl (X4 ) is presented in Table 1. The MINITAB® software (Minitab package version 16.0 Inc., USA) was applied for the experimental design. Analysis of variance was used for the data analysis to obtain the interaction between the process variables and the responses.
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2.6. Purification of bioflocculant
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Bioflocculant was purified according to the method of Sathiyanarayanan et al. [23]. The culture supernatant was centrifuged at 10,000 rpm for 20 min. To the obtained supernatant, three volumes of cold ethanol were added, instantly, till white cotton-like flocs were formed and then left to stand at 4 ◦ C overnight. The resulting precipitate was obtained by centrifugation at 10,000 rpm for 15 min. After repeating the purification process twice, the bioflocculant was dialyzed against de-ionized water at 4 ◦ C overnight and then lyophilized to obtain purified bioflocculant [23].
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2.7. Biosynthesis of silver nanoparticles
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The active bioflocculant producer MBRC-91 was freshly inoculated in statistically optimized production medium. The flasks were incubated at 30 ◦ C in a rotary shaker at 180 rpm for 96 h. After incubation, the fermentation broth was centrifuged at 10,000 rpm for 10 min. The bioflocculant was purified from culture supernatant and used for the synthesis of silver nanoparticles. The purified
Flocculation activity (%) Observed
Predicted
95.32 76.83 57.82 92.65 60.37 91.47 92.05 77.57 73.65 65.79 66.84 98.96 88.81 64.32 90.85 88.37 80.35 85.81 76.36 95.82 76.58 71.37 83.70 90.47 98.32 98.67 98.73
92.86 77.12 58.35 95.93 59.46 90.37 93.97 79.31 76.21 67.14 67.94 98.85 89.68 70.82 86.79 89.95 76.93 80.85 78.05 95.97 79.95 66.11 85.69 83.83 98.57 98.57 98.57
bioflocculant was added separately to the reaction vessel containing silver nitrate at a concentration of 10−3 M (1% (v/v)) and incubated in dark room condition for 96 h at 30 ◦ C, which resulted in a brown to yellow colour indicating the formation of silver nanoparticles. The synthesized silver nanoparticles were collected by high speed centrifugation and washed with double distilled water and dialyzed against water to get the pure silver nanoparticles. All the experiments were carried out in triplicate and average values have been reported. 2.8. Characterization of silver nanoparticles The synthesized silver nanoparticles were monitored by UV–vis spectroscopy using Shimadzu (Model No. UV-1800) double-beam UV–vis spectrophotometer. The synthesized silver nanoparticles were freeze–dried, powdered and used for XRD analysis (X’PertMPD, Philips, Netherlands) at 40 kV/30 mA using continuous scanning 2 mode. Synthesized silver nanoparticles were mounted on specimen stubs with double-sided adhesive tape coated with platinum in a sputter coater and examined under field emission scanning electron microscopy (FESEM) (JSM-6700, JEOL, Japan). For high resolution transmission electron microscopy (HRTEM) imaging, a drop of aqueous solution containing the silver nanoparticles were placed on carbon coated copper grids and dried under an infrared lamp (JEM 1010, JEOL, Japan) (AC voltage −80.0 kV). In addition, the presence of silver metals in the sample was analyzed by energy dispersive X-ray analysis (EDXA) combined with FESEM. The dried powder was diluted with potassium bromide in the ratio of 1:100 and recorded using the Fourier transform infrared spectroscopy (FTIR) (Perkin Elmer Inc., USA) and spectrum GX spectrometry within the range of 400–4000 cm−1 . 2.9. Antibacterial activity The antibacterial activity of the bioflocculant based synthesized silver nanoparticles against pathogenic bacteria such as Escherichia coli (ATCC 10536), Bacillus subtilis (ATCC 6633), Staphylococcus
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density was expressed as colony forming units (CFU/mL). The silver nanoparticles treated sewage water has the potent bactericidal activity in waste water treatment. All the experiments were carried out in triplicate and average values have been reported.
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aureus (ATCC 6538) and Pseudomonas aeruginosa (ATCC 27853) were measured using the well-diffusion method. Prior to the experiment, pure cultures were subcultured onto Müller–Hinton broth and incubated at 35 ◦ C on a rotary shaker at 180 rpm. Wells of 6-mm diameter were made on Müller–Hinton agar plates using gel puncture. Each strain was swabbed uniformly onto the individual plates using sterile cotton swabs. Using a micropipette, 10, 25, 50, 75 and 100 L of the samples of silver nanoparticles solution were poured onto each wells on all plates. After incubation the plates measured for zone of inhibition using a Vernier calliper in triplicate. Chloramphenicol and silver solution were used as controls.
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2.10. Controlling bacterial populations in sewage water
The marine environment provides a real untapped resource for novel actinobacteria. Totally 20 different marine actinobacteria were isolated from the marine sediment samples collected from the Busan coast, South Korea and were screened for their capability to produced bioflocculant. Based on the growth rate and high flocculating activity, MBRC-91 was considered as effective producer of bioflocculant. The active producer was identified using cultural, morphological, biochemical, physiological characteristics and 16S rDNA sequence (Fig. 1a). 16S rDNA sequence of bacterium strain MBRC-91 was deposited in NCBI (accession no. KC179813). 16S rDNA sequence similarity between the actinobacterial strain MBRC-91 and Streptomyces fimicarius was over 99%. Therefore, according to its morphological, physiological properties and 16S rDNA BLAST result, the isolated strain MBRC-91 was identified as a strain of Streptomyces sp.
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Sewage water was collected from Busan, South Korea. The total heterotrophic bacteria population in sewage water was enumerated by adopting the spread plate methods using nutrient agar medium to find out the bacterial population density before doing experiments. The different concentration of biosynthesized silver nanoparticles were prepared and mixed with sewage water and incubated for 12 h at room temperature. After the incubation period, the silver nanoparticles treated sewage water was enumerated by adopting the spread plate methods using nutrient agar medium. The silver nanoparticles treated sewage water was serially diluted (10−5 ) and 0.1 mL of the diluted suspension was spread over the surface of nutrient agar medium. After the incubation period, the colonies were counted and the bacterial population
3. Results and discussion 3.1. Isolation and molecular identification of marine actinobacteria
Fig. 1. (a) Phylogenetic relationships of strain MBRC-91 and other closely related Streptomyces species based on partial 16S rDNA sequence of MBRC-91. The tree was generated using the neighbor-joining method and the sequence from Lactobacillus mellis was considered as out-group. The data set was re-sampled 1000 times by using the bootstrap option and percentage values are given at the nodes. Bar, 0.05 substitutions per site. (b). Growth and flocculating activity of Streptomyces sp. MBRC-91.
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Table 2 Effects of the medium composition on the bioflocculant production. Carbon source
FA (%)
Nitrogen source
FA (%)
Metal ions
FA (%)
Temp (◦ C)
FA (%)
pH
FA (%)
Blank Starch Glucose Sucrose Palm jaggery Maltose Lactose
7.5 63.1 78.9 82.6 96.3 59.8 44.1
Blank Beef extract Yeast extract Peptone Malt extract Casein Soybean meal
28.9 58.3 84.6 60.1 53.8 72.6 79.4
Blank NaCl CaCl2 CuSO4 MgSO4 FeSO4 MnCl2
45.8 76.4 84.8 52.5 82.3 70.5 42.8
20 25 30 35 40 45 50
78.1 83.6 89.4 81.2 73.5 68.4 53.3
4.0 5.0 6.0 7.0 8.0 9.0 10.0
28.5 43.2 78.2 86.8 79.4 77.6 75.4
FA, Flocculating activity; Temp, temperature.
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3.2. Growth and time course profiles
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The correlation between bioflocculant production and culturing time may differ among different organisms. The flocculating rate curve is parallel to the growth curve and flocculating rates increase with increasing cultivation time, indicating the bioflocculant is produced by strain MBRC-91 during its growth (Fig. 1b). This is supported by the fact that the flocculating rates increased rapidly during the logarithmic growth and reached its maximum flocculating efficiency (98%) in stationary phase (96 h). Thus, the corresponding steady increase in cell growth is possibly an indication that the bioflocculant was produced by biosynthesis during growth and not by cell autolysis [25]. Further increase in the cultivation period resulted in a slight decrease in both flocculating activity and cell growth; this could be attributed to cell autolysis and/or the presence of a bioflocculant degrading enzyme [26]. Similar to our finding, bioflocculant production by Streptomyces sp. [27] and Bacillus licheniformis [8] which were all synchronous with cell growth and reached the maximum concentration in the stationary phase of the cell.
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3.3. Bioflocculant production
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the necessary nutrients for bioflocculant production and further influence the flocculating activity. The carbon source contributes to cell growth and metabolite synthesis during the microorganism cultivation. Among the carbon sources studied, palm jaggery was the most favorable for the flocculating activity (in a kaolin suspension), whereas the flocculation rates by sucrose, glucose, starch, maltose and lactose were lower (Table 2). A flocculating activity as high as 96.3% was obtained with palm jaggery as the carbon source. Palm jaggery was the most preferred and cheapest carbon source than commercial sugar and it enhanced the flocculating activity. As shown in Table 2, the nitrogen sources were sequenced by the flocculating activity as follows: yeast extract > soybean meal > casein > peptone > beef extract > malt extract > blank. Yeast extract was obviously better source for the flocculating activity
3.3.1. Effects of the carbon source, nitrogen source and metal ions on bioflocculant production Most microbial flocculants consist of polysaccharides, protein and nucleic acids. The medium carbon and nitrogen source provide Table 3 Analysis of variance (ANOVA) for the parameters of response surface methodology fitted to second-order polynomial model for optimization of flocculation activity. Source
DF
Seq SS
Adj SS
Adj MS
F-value
Regression Linear Square Interaction Residual error Lack-of-fit Pure error Total X1 X2 X3 X4 X 21 X 22 X 23 X 24 X1 X2 X1 X3 X1 X4 X2 X3 X2 X4 X3 X4
14 4 4 6 12 10 2 26 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3944.45 1152.96 955.77 1835.73 223.87 223.77 0.10 4168.32 357.74 184.79 197.88 412.54 120.98 84.03 32.33 718.43 710.76 49.00 399.60 121.11 35.88 519.38
3944.45 1152.96 955.77 1835.73 223.87 223.77 0.10
281.747 288.239 238.942 305.955 18.656 22.377 0.049
15.10 15.45 12.81 16.40
p-value > F 0.000 0.000 0.000 0.000
456.37
0.002
357.74 184.79 197.88 412.54 474.64 347.55 204.35 718.43 710.76 49.00 399.60 121.11 35.88 519.38
357.739 184.789 197.884 412.544 474.643 347.548 204.353 718.427 710.756 49.000 399.600 121.110 35.880 519.384
19.18 9.91 10.61 22.11 25.44 18.63 10.95 38.51 38.10 2.63 21.42 6.49 1.92 27.84
0.001 0.008 0.007 0.001 0.000 0.001 0.006 0.000 0.000 0.131 0.001 0.026 0.191 0.000
2 = 69.07; DF, degree of freedom; Seq SS, R2 = 94.63; CV = 1289.15; R2Adj = 88.36; RPre sequential sum of squares; Adj SS, adjusted sum of squares; Adj MS, adjusted mean square; F, Fischer’s test; p, probability value.
Fig. 2. Response surface plots (a) and contour plots (b) for the flocculating activity optimization.
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(84.6%). Cationic metal ions can neutralize the negative charges of both the polysaccharide and suspended particles and increase the adsorption of the polysaccharide onto the suspended particles [28]. The effect of the metal ions on the bioflocculant production was also investigated; most of the ions can promote the flocculant production to varying degrees, except for the negative effect of Mn2+ . The result showed that CaCl2 was the most favorable cation for bioflocculant production.
3.3.2. Effect of the initial pH and temperature on bioflocculation production The effects of the pH variation within the range of 4.0–10.0 on bioflocculation production are shown in Table 2. In the initial pH range of 6.0–10.0, bioflocculation maintained a high flocculating activity, and the optimum initial pH was 7.0. Table 2 also shows the relationship between temperature and flocculating activity. When the temperature was in the range of 20–50 ◦ C, the flocculating activity was above 50%, and the highest flocculating activity of 89.4% was achieved at 30 ◦ C. Flocculating activity decreased slightly, when the temperature exceeded 30 ◦ C. This can be explained by denaturalization of proteins in the bioflocculant and an increase in hot movement of kaolin particles [29].
3.3.3. Response surface methodology (RSM) Response surface methodology (RSM) is a statistical tool to optimize medium components and their interactive concentrations to enhance the productivity [30]. Based on Plackett–Burman design the most significant variables such as palm jaggery, yeast extract, K2 HPO4 and NaCl were identified from the 6 variables analyzed using one factor at a time experiment. The Box–Behnken design with three factors and three levels, including five replicates at the centre point, was used for fitting a second-order response surface (Table 3). The design matrix and the corresponding experimental data were shown in Table 3. The results were analyzed by standard analysis of variance (ANOVA) and following quadratic regression equations were obtained in terms of bioflocculation production. Using the designed experimental data, the polynomial model for protease yield Y was regressed by only considering the significant terms and was shown as below: Y=
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44.28 + 4.37X1 − 32.44X2 + 10.48X3 + 116.49X4 − 0.38X12 −8.07X22 − 1.55X32 −
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Fig. 3. (a) UV–absorbance spectra of biosynthesized silver nanoparticles using culture supernatant of Streptomyces sp. MBRC-91. (b) Energy dispersive X-ray spectrum of biosynthesized silver nanoparticles by Streptomyces sp. MBRC-91.
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where Y is the predicted flocculating activity, X1 the palm jaggery, X2 the yeast extract, X3 the K2 HPO4 , and X4 is the NaCl. The goodness of fit of a regression equation developed could be measured by determination coefficient. The R2 value of 94.63 and adjusted R2 of 88.36 showed that the model could be significant predicting the response and explaining 95% of the variability in the data. ANOVA table was illustrated (Table 3). The calculated F value (15.10) and a low p value (p = 0.000) demonstrated that the quadratic model was highly significant. From the table it was also observed that the coefficient of square, linear and interaction effects were also highly significant (p = 0.000). Smaller the probability (p) values, i.e. lesser than 0.05 (p < 0.05) and larger the magnitude of ‘t’ value was highly significant. The coefficients of this response, viz., X1 , X2 , X3 , X4 , X12 , X22 , X32 , X42 , X1 X2 , X1 X4 , X2 X3 and X3 X4 were found to be the most significant of this model (p < 0.05). Three-dimensional response surface plots and 2D contour plots graphically represent regression equations are generally used to demonstrate relationships between the response and experimental levels of each variable. Fig. 2 shows the response surface plots and their respective contour plots of bioflocculation production. Each figure presents the effect of two factors while the other factor was held at zero level. As shown in the surface plots, four was interaction between each pair of variables. All the interaction between the selected four variables were significant. According to the canonical analysis, the optimal concentrations of palm jaggery, yeast extract, K2 HPO4 , and NaCl were 18.73, 2.07, 3.74 and 0.38 g/L, respectively. The maximum bioflocculation production was estimated to be 98.96%, and the actual production
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obtained with the optimized medium was 98.85%, which is in close agreement to the model prediction. 3.4. Biosynthesis and characterization of silver nanoparticles The purified bioflocculant of Streptomyces sp. MBRC-91 when added to aqueous silver nitrate at room temperature resulted in the formation of silver nanoparticles. Silver nanoparticles exhibit yellowish brown color in aqueous solution due to excitation of surface plasmon vibrations in silver nanoparticles [31]. After overnight incubation in dark room condition the colorless reaction mixture was turned into a dark brown color solution which indicates the silver nanoparticles synthesis. Further confirmation of synthesis of silver nanoparticles was carried out by UV–vis spectroscopy using Shimadzu (Model No. UV-1800) double-beam UV–vis spectrophotometer. The primary characterization of synthesized nanoparticles by UV–vis spectroscopy has proven to be a very valuable technique for the analysis of nanoparticles [32]. UV–vis absorption spectra showed that the broad surface plasmon resonance at 420 nm (Fig. 3a). Previous studies have shown that the spherical silver nanoparticles contribute to the absorption bands at around 400–420 nm in the UV–vis spectra [33–35]. The results of EDXA analysis are shown in Fig. 3b. Elemental silver can be seen in the graph presented by the EDXA analysis in support of XRD results, which indicated the reduction of silver ions to elemental silver. XRD patterns of nanoparticles exhibit several size dependent features leading to peak position, heights and widths. The XRD pattern shows four intense peaks in the whole spectrum of 2 values ranging from 30 to 80 (Fig. 4a). It is
Fig. 4. (a) XRD pattern of biosynthesized silver nanoparticles exhibiting the facets of crystalline silver. (b) HR-TEM images of biosynthesized silver nanoparticles (50 nm scale) by Streptomyces sp. MBRC-91. (c) 20 nm scale and (d) selected area diffraction pattern.
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Fig. 5. Antibacterial activity of biosynthesized silver nanoparticles against pathogenic bacteria by well diffusion method.
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important to know the exact nature of the silver particles formed and this can be deduced from the XRD spectrum of the sample. XRD spectra of pure crystalline silver structures have been published by the Joint Committee on Powder Diffraction Standards (file no. 04-0783). A comparison of our XRD spectrum with the Standard confirmed that the silver particles formed in our experiments were in the form of nanocrystals, as evidenced by the peaks at 2 values of 38.25◦ , 44.37◦ , 64.60◦ and 77.64◦ , corresponding to 1 1 1, 2 0 0, 2 2 0, and 3 1 1 planes for silver, respectively. These interpretations were also similar to the results of Sukirtha et al. [36]. The morphology and size of the particles were determined by HRTEM. Fig. 4b–d shows that the particles are spherical in nature with particle range in size from 10 to 60 nm with an average particle size of 35 nm. Field emission scanning electron microscopy (FESEM) was also used to investigate the morphology and size of synthesized silver nanoparticles. The images showed the presence of spherical in nature. It is reported earlier that HRTEM images for the biosynthesized silver nanoparticles spherical in shape with an average size ranging from 10 to 100 nm [37,38]. Fourier transform infrared spectroscopy (FTIR) was used to identify the possible biomolecules present in Streptomyces sp. MBRC-91 which are responsible for capping and lead to efficient stabilization of silver nanoparticles. The FTIR spectral analysis reveals the presence of absorption peaks at 3428, 2071, 1634, 1067 and 687 cm−1 . The biosynthesized silver nanoparticles were observed in the presence of bands due to O–H stretching (3428 cm−1 ), C O stretching (2071 cm−1 ), N–H bend (1634 cm−1 ), C–N stretching (1067 cm−1 ) and C–Br stretching (687 cm−1 ). When the metal nanoparticles form in the solution, they must be stabilized against the Van der Waals forces of attraction which may otherwise cause coagulation. FTIR analysis data confirms the presence of O–H stretching (3428 cm− 1 ) which may be responsible for reducing metal ions into their respective nanoparticles.
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3.5. Antibacterial activity
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The antibacterial activity of biosynthesized silver nanoparticles (10, 25, 50, 75 and 100 L) was investigated against various pathogenic organisms such as E. coli, B. subtilis, S. aureus and P. aeruginosa using well-diffusion method. The mean inhibitory zone
of the four replicates of diameter was measured and to be tabulated (Fig. 5). Only the silver nanoparticles that exhibited significant antibacterial activity compared to silver solution and chloramphenicol. The highest antibacterial activity was observed against P. aeruginosa. The lower activity was found against B. subtilis. Sarkar et al. [39] reported that for E. coli and S. aureus, silver nanoparticles demonstrated greater bactericidal efficiency compared to penicillin [39]. 3.6. Controlling bacterial populations in sewage water Bioflocculant is a kind of biodegradable macromolecular flocculant secreted by microorganisms. Flocculation is an easy and effective method of removing suspended solids, colloids cell debris, etc. Various flocculants are employed for wastewater treatment, tap water production, dredging/downstream processes and other industrial fields [12] but in this present study a novel approach has been employed to reduce the total heterotrophic bacterial populations in sewage water using biosynthesized silver nanoparticles. It was recorded that initial 134 × 105 CFU/mL in sewage water and it was significantly controlled after silver nanoparticles treatment (0.30 mg/L) into 3 × 105 CFU/mL. These results suggest that biosynthesized silver nanoparticles have great potential as antibacterial activity in sewage water and this results could make a novel avenue in the wastewater treatment and this biosynthesized silver nanoparticles could be used along with bioflocculant in wastewater treatment process. 4. Conclusion In the present study, the marine actinobacteria are good candidates for bioflocculants and have been recognized as an important source of biological macromolecules that are of potential interest to various industrial applications. Optimization of the culture medium and growth conditions reduced the cost of medium components and improved the feasibility of commercial production. To our knowledge, this is the first report on the production and optimization of polysaccharide-based bioflocculant for the biosynthesis of silver nanoparticles by Streptomyces sp. MBRC-91. The biosynthesized silver nanoparticles were characterized by UV–vis
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spectroscopy, XRD, FTIR, FESEM, EDXA, and HRTEM and the present synthetic method is truly a greener approach. The biosynthesized silver nanoparticles have great potential as antibacterial activity in sewage water and this results could make a novel avenue in the wastewater treatment. In this research provides helpful insights into the development of novel bactericidal bionanomaterials for wastewater treatment and other biotechnological processes.
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Acknowledgment
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This paper was supported by research funds of Pukyong National University in 2015.
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References
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Contents lists available at ScienceDirect
International Journal of Biological Macromolecules journal homepage: www.elsevier.com/locate/ijbiomac
Production of polysaccharide-based bioflocculant for the synthesis of silver nanoparticles by Streptomyces sp
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Panchanathan Manivasagan a , Kyong-Hwa Kang a , Dong Gyu Kim b , Se-Kwon Kim a,c,∗ a
Marine Bioprocess Research Center, Pukyong National University, Busan 608-739, Republic of Korea Specialized Graduate School Science & Technology Convergence, Pukyong National University, Busan 608-739, Republic of Korea Specialized Graduate School Science & Technology Convergence, Department of Marine-Bio, Convergence Science, Pukyong National University, Busan 608-739, Republic of Korea b c
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Article history: Received 9 February 2015 Received in revised form 4 March 2015 Accepted 15 March 2015 Available online xxx
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Keywords: Bioflocculants Polysaccharides Silver nanoparticles Response surface methodology Streptomyces sp.
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1. Introduction
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Polysaccharide-based bioflocculants have attracted considerable attention in recent years due to their biodegradable, harmless and negligible secondary pollution. Bioflocculants are organic macromolecular substances secreted by microorganisms. A simple, cost-effective and green method was developed for the biosynthesis of silver nanoparticles using polysaccharides as reducing and stabilizing agents. In this paper, we report on the production and optimization of polysaccharide-based bioflocculant for the green synthesis of silver nanoparticles by Streptomyces sp. MBRC-91. Medium composition and culture conditions for polysaccharide-based bioflocculants were statistically optimized by response surface methodology (RSM). The bioflocculant production was statistically optimized with most significant factors, namely palm jaggery (18.73 g/L), yeast extract (2.07 g/L), K2 HPO4 (3.74 g/L) and NaCl (0.38 g/L), respectively. The biosynthesized silver nanoparticles were characterized by UV–vis spectroscopy, XRD, FTIR, FESEM, EDXA and HRTEM. The biosynthesized silver nanoparticles revealed strong antibacterial activity in sewage water and this result could make a new avenue in the wastewater treatment. Therefore, the biosynthesized silver nanoparticles can be extended as an alternative for the development of new bactericidal bionanomaterials for wastewater treatment and biotechnological applications. © 2015 Published by Elsevier B.V.
Marine microbial-produced bioflocculants have received considerable scientific and biotechnological attention in recent years due to their biodegradability, non-toxic and their degradation intermediates are not secondary pollutants [1,2]. Bioflocculants are marine microorganism-produced natural organic macromolecule substances that can flocculate suspended solids, cells, colloidal solids, etc. To date, many bioflocculant-producing marine microorganisms have been reported, including bacteria [3], actinobacteria [4], algae [5], fungi [6] and yeast [7]. Bioflocculants are mainly composed of biological macromolecules such as polysaccharide, protein, glycoprotein, nucleic acid, etc. [8]. Among these biological macromolecules, the polysaccharide-based bioflocculants received
∗ Corresponding author at: Marine Bioprocess Research Center, Pukyong National University, Busan 608-739, Republic of Korea. Tel.: +82 51 629 6870; fax: +82 51 629 6865. E-mail addresses: [email protected] (P. Manivasagan), [email protected] (S.-K. Kim).
considerable attention in recent years due to their high efficiency in disposing of various kinds of pollutants, including dyeing pigment, heavy metal ion and other suspended pollutants [9–11]. Bioflocculants have been widely used in industrial processes, including wastewater treatment, downstream processing, food and fermentation process [12]. The marine environment is not yet fully explored, it is a huge treasure trove of marine actinobacteria resources [13,14]. Nowadays, marine actinobacteria are the best sources of secondary metabolites with various biological activities. Many representatives of the order Actinomycetales are prolific producers of thousands of biologically active secondary metabolites [15]. Among the actinobacteria, streptomycetes group is considered most economically important because, out of the approximately more than 11,900 known antibiotics, 50–55% is produced by Streptomyces [16]. Marine actinobacteria, particularly, streptomycetes have attracted much attention because of their ability to make secondary metabolites such as antibiotics, immunosuppressors, and many other bioactive compounds [17]. Exploitation of Streptomyces species in marine bio-nanotechnology has also recently received considerable attention, owing to their various applications [18]. Marine
http://dx.doi.org/10.1016/j.ijbiomac.2015.03.022 0141-8130/© 2015 Published by Elsevier B.V.
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actinobacterial synthesis of metal nanoparticles has good potential to develop simple, cost-effective and eco-friendly methods for production of important bionanomaterials [19,20]. Silver nanoparticles are widely investigated owing to their broad range of application as antibacterial, catalyst and as a biosensor [21]. Polysaccharides are the polymers of monosaccharides. In nature, polysaccharides have various resources from algae, plants, animals and microorganisms were used as reducing and stabilizing agents for the biosynthesis of metal nanoparticles [22]. Polysaccharides are highly stable, safe, non-toxic, hydrophilic and biodegradable. Polysaccharide-based bioflocculants can be used for high-performance nanomaterials production, since they easily form a variety of liquid crystals in aqueous solutions and bioflocculant-mediated processes are highly profitable. The production of polysaccharide-based bioflocculants is not species specific and each strain of same species produces different kinds of polysaccharides with different biological applications [23]. Therefore, this study involves the production and optimization of polysaccharide-based bioflocculant for the green synthesis of silver nanoparticles using Streptomyces sp. MBRC-91. To the best of our knowledge, this marine actinobacterium Streptomyces sp. MBRC91 has never been used for the production of polysaccharide-based bioflocculant for the green synthesis of silver nanoparticles.
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and incubated at 30 ◦ C with shaking at 180 rpm for 96 h. Samples were withdrawn at different time intervals and monitored for cell growth and flocculating activity. Culture broth was centrifuged at 10,000 rpm for 20 min to separate the cells which were washed twice with distilled water and dried at 65 ◦ C to constant weight as a measurement of cell growth. The culture supernatant was used to study flocculating activity [24]. All experiments were performed in triplicates for the mean calculation.
2.4. Determination of flocculating activity Flocculating activity was determined using the previous method with a slight modification [8]. Kaolin clay was chosen as the suspended solid to calculate the flocculating activity. Kaolin clay was chosen as the suspended solid to calculate the flocculating activity. First, 93 mL kaolin suspension (4.0 g/L), 5.0 mL of a 1% CaCl2 (w/v) and 2.0 mL of a culture supernatant were mixed and stirred at 4 min and then allowed to incubate for 5 min. After incubation, the supernatant absorbance was measured by a spectrophotometer at 550 nm (Shimadzu No. UV-1800, Japan). The culture supernatant was replaced with a culture medium at the same concentration in the control experiment. The flocculating activity was calculated according to equation. flocculating activity (%) =
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2.1. Chemicals Silver nitrate was purchased from Sigma-Aldrich (St. Louis, USA). All media components were purchased from Lab M Limited (Bury, UK). All chemicals were of analytical grade and procured from Sigma-Aldrich (St. Louis, USA). 2.2. Isolation and molecular identification of marine actinobacteria
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A total of 20 marine actinobacterial strains were isolated from the marine sediment samples collected from the Busan coast (Latitude 35◦ 09 N; Longitude 129◦ 07 E), South Korea. All the strains were grown on starch casein agar medium and maintained as spore suspension. The composition of the bioflocculant screening medium was as follows: glucose, 10 g/L; yeast extract, 0.5 g/L; KH2 PO4 , 2 g/L; K2 HPO4 , 5 g/L; (NH4 )2 SO4 , 0.2 g/L; urea, 0.5 g/L and NaCl, 0.5 g/L with the initial pH 7.0. Each isolated strain was inoculated in 250 mL Erlenmeyer flasks containing 50 mL of screening medium and incubated at 30 ◦ C in a rotary shaker at 180 rpm for 96 h. After the incubation period, the culture supernatants were determined for flocculating activity. The strain MBRC-91 with the highest flocculating activity was selected for further investigation. The isolate MBRC-91 was identified based on 16S rDNA sequencing. Its isolated genomic DNA was subjected to PCR amplification of 16S rDNA using universal primers. Amplified product was gel purified using QIAquick gel extraction kit and purified products were sequenced. The 16S rDNA sequence (∼1493 bp) was compared with currently available actinobacteria sequences in GenBank. The GenBank accession numbers of strain MBRC-91 is KC179813.
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Production of bioflocculant by Streptomyces sp. MBRC-91 was performed in 500 mL Erlenmeyer flasks containing 100 mL of the production medium with 180 rpm at 30 ◦ C. The composition of the modified production medium was as follows: palm jaggery, 20 g/L; yeast extract, 2.5 g/L; NH4 NO2 , 1.0 g/L; MgCl2 , 0.2 g/L; K2 HPO4 , 5 g/L and NaCl, 0.5 g/L. The initial pH value of the medium was adjusted to 7.0. Each flask was inoculated with 4% (v/v) of the seed culture
(A − B) × 100 A
where A and B were OD550 of the control and of the sample supernatant, respectively.
2.5. Statistical optimization of bioflocculant production using response surface methodology Plackett–Burman design is a powerful tool used to screen n variables in only n + 1 experiment and this design significantly decreases the number of experiments needed to efficiently achieve experimental goals. In the present study, 6 independent medium components were investigated using Plackett–Burman design to identify the components that significantly affected bioflocculant production. The components included palm jaggery, yeast extract, NH4 NO2 , MgCl2 , K2 HPO4 , and NaCl. The design was developed by the MINITAB® software (Minitab package version 16.0 Inc., USA). All the variables were evaluated in twelve experimental trials and the average bioflocculant yield for each trial was used as the response variable. Regression analysis determined the variables that had a significant (95% level (p < 0.05)) effect on bioflocculant yield, and these variables were evaluated in further optimization experiments. After the critical medium components were identified by PB design, RSM was employed to optimize the component concentration to improve bioflocculant production. Six variables were selected from Plackett–Burman design that significantly affected bioflocculant production was further optimized by response surface methodology (RSM). A Box–Behnken design of the experiments was formulated to investigate four flocculation parameters. Each 250 mL culture of Streptomyces sp. MBRC-91 was added in 500 mL Erlenmeyer flasks and incubated at 30 ◦ C in a rotary shaker at 180 rpm for 96 h. The parameters were set according to the orthogonal values of Box–Behnken design (Table 1). The experiments were performed in triplicate. RSM is known to evaluate the interaction between the significant factors of an experiment and optimize them. Four-level factor experiment setup was designed using MINITAB® software (Minitab package version 16.0 Inc., USA) and the quality of analysis model was based on an analysis of variance (ANOVA). The response variable (Y), representing the bioflocculation activity, was fitted
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Table 1 Experimental design and results of Box–Behnken optimization experiment.
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−1 (10) +1 (20) −1 (10) +1 (20) 0 (15) 0 (15) 0 (15) 0 (15) −1 (10) +1 (20) −1 (10) +1 (20) 0 (15) 0 (15) 0 (15) 0 (15) −1 (10) +1 (20) −1 (10) +1 (20) 0 (15) 0 (15) 0 (15) 0 (15) 0 (15) 0 (15) 0 (15)
−1 (0.5) −1 (0.5) +1 (2.5) +1 (2.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) −1 (0.5) +1 (2.5) −1 (0.5) +1 (2.5) 0 (1.5) 0 (1.5) 0 (1.5) 0 (1.5) −1 (0.5) +1 (2.5) −1 (0.5) +1 (2.5) 0 (1.5) 0 (1.5) 0 (1.5)
0 (3) 0 (3) 0 (3) 0 (3) −1 (1) +1 (5) −1 (1) +1 (5) 0 (3) 0 (3) 0 (3) 0 (3) −1 (1) −1 (1) +1 (5) +1 (5) −1 (1) −1 (1) +1 (5) +1 (5) 0 (3) 0 (3) 0 (3) 0 (3) 0 (3) 0 (3) 0 (3)
0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) −1 (0.1) −1 (0.1) +1 (0.5) +1 (0.5) −1 (0.1) −1 (0.1) +1 (0.5) +1 (0.5) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) −1 (0.1) −1 (0.1) +1 (0.5) +1 (0.5) 0 (0.3) 0 (0.3) 0 (0.3)
using a second-order polynomial equation given as Y=
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ˇ0 + ˇ1 X1 + ˇ2 X2 + ˇ3 X3 + ˇ4 X4 + ˇ11 X12
+ ˇ22 X22
+ ˇ33 X32
+ˇ44 X42 + ˇ12 X1 X2 +ˇ13 X1 X3 + ˇ14 X1 X4 + ˇ23 X2 X3 + ˇ24 X2 X4 + ˇ34 X3 X4
177
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where Y is the predicted response, ˇ0 the constant, X1 –X4 the input variables, ˇ1 –ˇ4 the linear coefficient, ˇ11 –ˇ44 the quadratic coefficients and ˇ12 –ˇ44 are the second-order interactive coefficients. The actual value of coded levels of different parameters which are palm jaggery (X1 ), yeast extract (X2 ), K2 HPO4 (X3 ) and NaCl (X4 ) is presented in Table 1. The MINITAB® software (Minitab package version 16.0 Inc., USA) was applied for the experimental design. Analysis of variance was used for the data analysis to obtain the interaction between the process variables and the responses.
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2.6. Purification of bioflocculant
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Bioflocculant was purified according to the method of Sathiyanarayanan et al. [23]. The culture supernatant was centrifuged at 10,000 rpm for 20 min. To the obtained supernatant, three volumes of cold ethanol were added, instantly, till white cotton-like flocs were formed and then left to stand at 4 ◦ C overnight. The resulting precipitate was obtained by centrifugation at 10,000 rpm for 15 min. After repeating the purification process twice, the bioflocculant was dialyzed against de-ionized water at 4 ◦ C overnight and then lyophilized to obtain purified bioflocculant [23].
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2.7. Biosynthesis of silver nanoparticles
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The active bioflocculant producer MBRC-91 was freshly inoculated in statistically optimized production medium. The flasks were incubated at 30 ◦ C in a rotary shaker at 180 rpm for 96 h. After incubation, the fermentation broth was centrifuged at 10,000 rpm for 10 min. The bioflocculant was purified from culture supernatant and used for the synthesis of silver nanoparticles. The purified
Flocculation activity (%) Observed
Predicted
95.32 76.83 57.82 92.65 60.37 91.47 92.05 77.57 73.65 65.79 66.84 98.96 88.81 64.32 90.85 88.37 80.35 85.81 76.36 95.82 76.58 71.37 83.70 90.47 98.32 98.67 98.73
92.86 77.12 58.35 95.93 59.46 90.37 93.97 79.31 76.21 67.14 67.94 98.85 89.68 70.82 86.79 89.95 76.93 80.85 78.05 95.97 79.95 66.11 85.69 83.83 98.57 98.57 98.57
bioflocculant was added separately to the reaction vessel containing silver nitrate at a concentration of 10−3 M (1% (v/v)) and incubated in dark room condition for 96 h at 30 ◦ C, which resulted in a brown to yellow colour indicating the formation of silver nanoparticles. The synthesized silver nanoparticles were collected by high speed centrifugation and washed with double distilled water and dialyzed against water to get the pure silver nanoparticles. All the experiments were carried out in triplicate and average values have been reported. 2.8. Characterization of silver nanoparticles The synthesized silver nanoparticles were monitored by UV–vis spectroscopy using Shimadzu (Model No. UV-1800) double-beam UV–vis spectrophotometer. The synthesized silver nanoparticles were freeze–dried, powdered and used for XRD analysis (X’PertMPD, Philips, Netherlands) at 40 kV/30 mA using continuous scanning 2 mode. Synthesized silver nanoparticles were mounted on specimen stubs with double-sided adhesive tape coated with platinum in a sputter coater and examined under field emission scanning electron microscopy (FESEM) (JSM-6700, JEOL, Japan). For high resolution transmission electron microscopy (HRTEM) imaging, a drop of aqueous solution containing the silver nanoparticles were placed on carbon coated copper grids and dried under an infrared lamp (JEM 1010, JEOL, Japan) (AC voltage −80.0 kV). In addition, the presence of silver metals in the sample was analyzed by energy dispersive X-ray analysis (EDXA) combined with FESEM. The dried powder was diluted with potassium bromide in the ratio of 1:100 and recorded using the Fourier transform infrared spectroscopy (FTIR) (Perkin Elmer Inc., USA) and spectrum GX spectrometry within the range of 400–4000 cm−1 . 2.9. Antibacterial activity The antibacterial activity of the bioflocculant based synthesized silver nanoparticles against pathogenic bacteria such as Escherichia coli (ATCC 10536), Bacillus subtilis (ATCC 6633), Staphylococcus
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density was expressed as colony forming units (CFU/mL). The silver nanoparticles treated sewage water has the potent bactericidal activity in waste water treatment. All the experiments were carried out in triplicate and average values have been reported.
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aureus (ATCC 6538) and Pseudomonas aeruginosa (ATCC 27853) were measured using the well-diffusion method. Prior to the experiment, pure cultures were subcultured onto Müller–Hinton broth and incubated at 35 ◦ C on a rotary shaker at 180 rpm. Wells of 6-mm diameter were made on Müller–Hinton agar plates using gel puncture. Each strain was swabbed uniformly onto the individual plates using sterile cotton swabs. Using a micropipette, 10, 25, 50, 75 and 100 L of the samples of silver nanoparticles solution were poured onto each wells on all plates. After incubation the plates measured for zone of inhibition using a Vernier calliper in triplicate. Chloramphenicol and silver solution were used as controls.
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2.10. Controlling bacterial populations in sewage water
The marine environment provides a real untapped resource for novel actinobacteria. Totally 20 different marine actinobacteria were isolated from the marine sediment samples collected from the Busan coast, South Korea and were screened for their capability to produced bioflocculant. Based on the growth rate and high flocculating activity, MBRC-91 was considered as effective producer of bioflocculant. The active producer was identified using cultural, morphological, biochemical, physiological characteristics and 16S rDNA sequence (Fig. 1a). 16S rDNA sequence of bacterium strain MBRC-91 was deposited in NCBI (accession no. KC179813). 16S rDNA sequence similarity between the actinobacterial strain MBRC-91 and Streptomyces fimicarius was over 99%. Therefore, according to its morphological, physiological properties and 16S rDNA BLAST result, the isolated strain MBRC-91 was identified as a strain of Streptomyces sp.
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Sewage water was collected from Busan, South Korea. The total heterotrophic bacteria population in sewage water was enumerated by adopting the spread plate methods using nutrient agar medium to find out the bacterial population density before doing experiments. The different concentration of biosynthesized silver nanoparticles were prepared and mixed with sewage water and incubated for 12 h at room temperature. After the incubation period, the silver nanoparticles treated sewage water was enumerated by adopting the spread plate methods using nutrient agar medium. The silver nanoparticles treated sewage water was serially diluted (10−5 ) and 0.1 mL of the diluted suspension was spread over the surface of nutrient agar medium. After the incubation period, the colonies were counted and the bacterial population
3. Results and discussion 3.1. Isolation and molecular identification of marine actinobacteria
Fig. 1. (a) Phylogenetic relationships of strain MBRC-91 and other closely related Streptomyces species based on partial 16S rDNA sequence of MBRC-91. The tree was generated using the neighbor-joining method and the sequence from Lactobacillus mellis was considered as out-group. The data set was re-sampled 1000 times by using the bootstrap option and percentage values are given at the nodes. Bar, 0.05 substitutions per site. (b). Growth and flocculating activity of Streptomyces sp. MBRC-91.
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Table 2 Effects of the medium composition on the bioflocculant production. Carbon source
FA (%)
Nitrogen source
FA (%)
Metal ions
FA (%)
Temp (◦ C)
FA (%)
pH
FA (%)
Blank Starch Glucose Sucrose Palm jaggery Maltose Lactose
7.5 63.1 78.9 82.6 96.3 59.8 44.1
Blank Beef extract Yeast extract Peptone Malt extract Casein Soybean meal
28.9 58.3 84.6 60.1 53.8 72.6 79.4
Blank NaCl CaCl2 CuSO4 MgSO4 FeSO4 MnCl2
45.8 76.4 84.8 52.5 82.3 70.5 42.8
20 25 30 35 40 45 50
78.1 83.6 89.4 81.2 73.5 68.4 53.3
4.0 5.0 6.0 7.0 8.0 9.0 10.0
28.5 43.2 78.2 86.8 79.4 77.6 75.4
FA, Flocculating activity; Temp, temperature.
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3.2. Growth and time course profiles
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The correlation between bioflocculant production and culturing time may differ among different organisms. The flocculating rate curve is parallel to the growth curve and flocculating rates increase with increasing cultivation time, indicating the bioflocculant is produced by strain MBRC-91 during its growth (Fig. 1b). This is supported by the fact that the flocculating rates increased rapidly during the logarithmic growth and reached its maximum flocculating efficiency (98%) in stationary phase (96 h). Thus, the corresponding steady increase in cell growth is possibly an indication that the bioflocculant was produced by biosynthesis during growth and not by cell autolysis [25]. Further increase in the cultivation period resulted in a slight decrease in both flocculating activity and cell growth; this could be attributed to cell autolysis and/or the presence of a bioflocculant degrading enzyme [26]. Similar to our finding, bioflocculant production by Streptomyces sp. [27] and Bacillus licheniformis [8] which were all synchronous with cell growth and reached the maximum concentration in the stationary phase of the cell.
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3.3. Bioflocculant production
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the necessary nutrients for bioflocculant production and further influence the flocculating activity. The carbon source contributes to cell growth and metabolite synthesis during the microorganism cultivation. Among the carbon sources studied, palm jaggery was the most favorable for the flocculating activity (in a kaolin suspension), whereas the flocculation rates by sucrose, glucose, starch, maltose and lactose were lower (Table 2). A flocculating activity as high as 96.3% was obtained with palm jaggery as the carbon source. Palm jaggery was the most preferred and cheapest carbon source than commercial sugar and it enhanced the flocculating activity. As shown in Table 2, the nitrogen sources were sequenced by the flocculating activity as follows: yeast extract > soybean meal > casein > peptone > beef extract > malt extract > blank. Yeast extract was obviously better source for the flocculating activity
3.3.1. Effects of the carbon source, nitrogen source and metal ions on bioflocculant production Most microbial flocculants consist of polysaccharides, protein and nucleic acids. The medium carbon and nitrogen source provide Table 3 Analysis of variance (ANOVA) for the parameters of response surface methodology fitted to second-order polynomial model for optimization of flocculation activity. Source
DF
Seq SS
Adj SS
Adj MS
F-value
Regression Linear Square Interaction Residual error Lack-of-fit Pure error Total X1 X2 X3 X4 X 21 X 22 X 23 X 24 X1 X2 X1 X3 X1 X4 X2 X3 X2 X4 X3 X4
14 4 4 6 12 10 2 26 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3944.45 1152.96 955.77 1835.73 223.87 223.77 0.10 4168.32 357.74 184.79 197.88 412.54 120.98 84.03 32.33 718.43 710.76 49.00 399.60 121.11 35.88 519.38
3944.45 1152.96 955.77 1835.73 223.87 223.77 0.10
281.747 288.239 238.942 305.955 18.656 22.377 0.049
15.10 15.45 12.81 16.40
p-value > F 0.000 0.000 0.000 0.000
456.37
0.002
357.74 184.79 197.88 412.54 474.64 347.55 204.35 718.43 710.76 49.00 399.60 121.11 35.88 519.38
357.739 184.789 197.884 412.544 474.643 347.548 204.353 718.427 710.756 49.000 399.600 121.110 35.880 519.384
19.18 9.91 10.61 22.11 25.44 18.63 10.95 38.51 38.10 2.63 21.42 6.49 1.92 27.84
0.001 0.008 0.007 0.001 0.000 0.001 0.006 0.000 0.000 0.131 0.001 0.026 0.191 0.000
2 = 69.07; DF, degree of freedom; Seq SS, R2 = 94.63; CV = 1289.15; R2Adj = 88.36; RPre sequential sum of squares; Adj SS, adjusted sum of squares; Adj MS, adjusted mean square; F, Fischer’s test; p, probability value.
Fig. 2. Response surface plots (a) and contour plots (b) for the flocculating activity optimization.
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(84.6%). Cationic metal ions can neutralize the negative charges of both the polysaccharide and suspended particles and increase the adsorption of the polysaccharide onto the suspended particles [28]. The effect of the metal ions on the bioflocculant production was also investigated; most of the ions can promote the flocculant production to varying degrees, except for the negative effect of Mn2+ . The result showed that CaCl2 was the most favorable cation for bioflocculant production.
3.3.2. Effect of the initial pH and temperature on bioflocculation production The effects of the pH variation within the range of 4.0–10.0 on bioflocculation production are shown in Table 2. In the initial pH range of 6.0–10.0, bioflocculation maintained a high flocculating activity, and the optimum initial pH was 7.0. Table 2 also shows the relationship between temperature and flocculating activity. When the temperature was in the range of 20–50 ◦ C, the flocculating activity was above 50%, and the highest flocculating activity of 89.4% was achieved at 30 ◦ C. Flocculating activity decreased slightly, when the temperature exceeded 30 ◦ C. This can be explained by denaturalization of proteins in the bioflocculant and an increase in hot movement of kaolin particles [29].
3.3.3. Response surface methodology (RSM) Response surface methodology (RSM) is a statistical tool to optimize medium components and their interactive concentrations to enhance the productivity [30]. Based on Plackett–Burman design the most significant variables such as palm jaggery, yeast extract, K2 HPO4 and NaCl were identified from the 6 variables analyzed using one factor at a time experiment. The Box–Behnken design with three factors and three levels, including five replicates at the centre point, was used for fitting a second-order response surface (Table 3). The design matrix and the corresponding experimental data were shown in Table 3. The results were analyzed by standard analysis of variance (ANOVA) and following quadratic regression equations were obtained in terms of bioflocculation production. Using the designed experimental data, the polynomial model for protease yield Y was regressed by only considering the significant terms and was shown as below: Y=
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44.28 + 4.37X1 − 32.44X2 + 10.48X3 + 116.49X4 − 0.38X12 −8.07X22 − 1.55X32 −
290.16X42 + 2.67X1 X2 + 0.35X1 X3 + 10.0X1 X4 + 2.75X2 X3
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+14.98X2 X4 − 28.49X3 X4
Fig. 3. (a) UV–absorbance spectra of biosynthesized silver nanoparticles using culture supernatant of Streptomyces sp. MBRC-91. (b) Energy dispersive X-ray spectrum of biosynthesized silver nanoparticles by Streptomyces sp. MBRC-91.
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where Y is the predicted flocculating activity, X1 the palm jaggery, X2 the yeast extract, X3 the K2 HPO4 , and X4 is the NaCl. The goodness of fit of a regression equation developed could be measured by determination coefficient. The R2 value of 94.63 and adjusted R2 of 88.36 showed that the model could be significant predicting the response and explaining 95% of the variability in the data. ANOVA table was illustrated (Table 3). The calculated F value (15.10) and a low p value (p = 0.000) demonstrated that the quadratic model was highly significant. From the table it was also observed that the coefficient of square, linear and interaction effects were also highly significant (p = 0.000). Smaller the probability (p) values, i.e. lesser than 0.05 (p < 0.05) and larger the magnitude of ‘t’ value was highly significant. The coefficients of this response, viz., X1 , X2 , X3 , X4 , X12 , X22 , X32 , X42 , X1 X2 , X1 X4 , X2 X3 and X3 X4 were found to be the most significant of this model (p < 0.05). Three-dimensional response surface plots and 2D contour plots graphically represent regression equations are generally used to demonstrate relationships between the response and experimental levels of each variable. Fig. 2 shows the response surface plots and their respective contour plots of bioflocculation production. Each figure presents the effect of two factors while the other factor was held at zero level. As shown in the surface plots, four was interaction between each pair of variables. All the interaction between the selected four variables were significant. According to the canonical analysis, the optimal concentrations of palm jaggery, yeast extract, K2 HPO4 , and NaCl were 18.73, 2.07, 3.74 and 0.38 g/L, respectively. The maximum bioflocculation production was estimated to be 98.96%, and the actual production
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obtained with the optimized medium was 98.85%, which is in close agreement to the model prediction. 3.4. Biosynthesis and characterization of silver nanoparticles The purified bioflocculant of Streptomyces sp. MBRC-91 when added to aqueous silver nitrate at room temperature resulted in the formation of silver nanoparticles. Silver nanoparticles exhibit yellowish brown color in aqueous solution due to excitation of surface plasmon vibrations in silver nanoparticles [31]. After overnight incubation in dark room condition the colorless reaction mixture was turned into a dark brown color solution which indicates the silver nanoparticles synthesis. Further confirmation of synthesis of silver nanoparticles was carried out by UV–vis spectroscopy using Shimadzu (Model No. UV-1800) double-beam UV–vis spectrophotometer. The primary characterization of synthesized nanoparticles by UV–vis spectroscopy has proven to be a very valuable technique for the analysis of nanoparticles [32]. UV–vis absorption spectra showed that the broad surface plasmon resonance at 420 nm (Fig. 3a). Previous studies have shown that the spherical silver nanoparticles contribute to the absorption bands at around 400–420 nm in the UV–vis spectra [33–35]. The results of EDXA analysis are shown in Fig. 3b. Elemental silver can be seen in the graph presented by the EDXA analysis in support of XRD results, which indicated the reduction of silver ions to elemental silver. XRD patterns of nanoparticles exhibit several size dependent features leading to peak position, heights and widths. The XRD pattern shows four intense peaks in the whole spectrum of 2 values ranging from 30 to 80 (Fig. 4a). It is
Fig. 4. (a) XRD pattern of biosynthesized silver nanoparticles exhibiting the facets of crystalline silver. (b) HR-TEM images of biosynthesized silver nanoparticles (50 nm scale) by Streptomyces sp. MBRC-91. (c) 20 nm scale and (d) selected area diffraction pattern.
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Fig. 5. Antibacterial activity of biosynthesized silver nanoparticles against pathogenic bacteria by well diffusion method.
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important to know the exact nature of the silver particles formed and this can be deduced from the XRD spectrum of the sample. XRD spectra of pure crystalline silver structures have been published by the Joint Committee on Powder Diffraction Standards (file no. 04-0783). A comparison of our XRD spectrum with the Standard confirmed that the silver particles formed in our experiments were in the form of nanocrystals, as evidenced by the peaks at 2 values of 38.25◦ , 44.37◦ , 64.60◦ and 77.64◦ , corresponding to 1 1 1, 2 0 0, 2 2 0, and 3 1 1 planes for silver, respectively. These interpretations were also similar to the results of Sukirtha et al. [36]. The morphology and size of the particles were determined by HRTEM. Fig. 4b–d shows that the particles are spherical in nature with particle range in size from 10 to 60 nm with an average particle size of 35 nm. Field emission scanning electron microscopy (FESEM) was also used to investigate the morphology and size of synthesized silver nanoparticles. The images showed the presence of spherical in nature. It is reported earlier that HRTEM images for the biosynthesized silver nanoparticles spherical in shape with an average size ranging from 10 to 100 nm [37,38]. Fourier transform infrared spectroscopy (FTIR) was used to identify the possible biomolecules present in Streptomyces sp. MBRC-91 which are responsible for capping and lead to efficient stabilization of silver nanoparticles. The FTIR spectral analysis reveals the presence of absorption peaks at 3428, 2071, 1634, 1067 and 687 cm−1 . The biosynthesized silver nanoparticles were observed in the presence of bands due to O–H stretching (3428 cm−1 ), C O stretching (2071 cm−1 ), N–H bend (1634 cm−1 ), C–N stretching (1067 cm−1 ) and C–Br stretching (687 cm−1 ). When the metal nanoparticles form in the solution, they must be stabilized against the Van der Waals forces of attraction which may otherwise cause coagulation. FTIR analysis data confirms the presence of O–H stretching (3428 cm− 1 ) which may be responsible for reducing metal ions into their respective nanoparticles.
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3.5. Antibacterial activity
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The antibacterial activity of biosynthesized silver nanoparticles (10, 25, 50, 75 and 100 L) was investigated against various pathogenic organisms such as E. coli, B. subtilis, S. aureus and P. aeruginosa using well-diffusion method. The mean inhibitory zone
of the four replicates of diameter was measured and to be tabulated (Fig. 5). Only the silver nanoparticles that exhibited significant antibacterial activity compared to silver solution and chloramphenicol. The highest antibacterial activity was observed against P. aeruginosa. The lower activity was found against B. subtilis. Sarkar et al. [39] reported that for E. coli and S. aureus, silver nanoparticles demonstrated greater bactericidal efficiency compared to penicillin [39]. 3.6. Controlling bacterial populations in sewage water Bioflocculant is a kind of biodegradable macromolecular flocculant secreted by microorganisms. Flocculation is an easy and effective method of removing suspended solids, colloids cell debris, etc. Various flocculants are employed for wastewater treatment, tap water production, dredging/downstream processes and other industrial fields [12] but in this present study a novel approach has been employed to reduce the total heterotrophic bacterial populations in sewage water using biosynthesized silver nanoparticles. It was recorded that initial 134 × 105 CFU/mL in sewage water and it was significantly controlled after silver nanoparticles treatment (0.30 mg/L) into 3 × 105 CFU/mL. These results suggest that biosynthesized silver nanoparticles have great potential as antibacterial activity in sewage water and this results could make a novel avenue in the wastewater treatment and this biosynthesized silver nanoparticles could be used along with bioflocculant in wastewater treatment process. 4. Conclusion In the present study, the marine actinobacteria are good candidates for bioflocculants and have been recognized as an important source of biological macromolecules that are of potential interest to various industrial applications. Optimization of the culture medium and growth conditions reduced the cost of medium components and improved the feasibility of commercial production. To our knowledge, this is the first report on the production and optimization of polysaccharide-based bioflocculant for the biosynthesis of silver nanoparticles by Streptomyces sp. MBRC-91. The biosynthesized silver nanoparticles were characterized by UV–vis
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spectroscopy, XRD, FTIR, FESEM, EDXA, and HRTEM and the present synthetic method is truly a greener approach. The biosynthesized silver nanoparticles have great potential as antibacterial activity in sewage water and this results could make a novel avenue in the wastewater treatment. In this research provides helpful insights into the development of novel bactericidal bionanomaterials for wastewater treatment and other biotechnological processes.
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Acknowledgment
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This paper was supported by research funds of Pukyong National University in 2015.
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References
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Please cite this article in press as: P. Manivasagan, et al., Int. J. Biol. Macromol. (2015), http://dx.doi.org/10.1016/j.ijbiomac.2015.03.022
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