Bio-degradation of Bisphenol A by Pseudomonas

Journal of Radiation Research and Applied Sciences 11 (2018) 56e65

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Bio-degradation of Bisphenol A by Pseudomonas aeruginosa PAb1 isolated from effluent of thermal paper industry: Kinetic modeling and process optimization V. Vijayalakshmi a, *, P. Senthilkumar a, K. Mophin-Kani b, S. Sivamani c, N. Sivarajasekar d, S. Vasantharaj a a

PG & Research Dept. of Biotechnology, Hindusthan College of Arts & Science, Coimbatore 641028, TN, India PG Department of Civil Engineering, UKF College of Engineering & Technology, Parippally, Kollam 691302, Kerala, India Chemical and Petrochemical Engineering Section, Engineering Department, Salalah College of Technology, Thumrait Road, Salalah 211, Sultanate of Oman d Department of Biotechnology, Kumaraguru College of Technology, Coimbatore 641049, TN, India b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 May 2017 Received in revised form 26 July 2017 Accepted 13 August 2017 Available online 12 September 2017

A bacterium isolated from effluent of thermal paper industry and identified as Pseudomonas aeruginosa (PAb1) based on 16SrRNA gene sequence analysis which could grow on basal mineral salt medium upon bisphenol A, which functions as an exclusive carbon source. Physicochemical variables of thermal paper industry effluent noted were significantly greater than the typical limit due to pollution of the acquiring water systems. The mathematic kinetic models like Monod, Moser and Tesier models were applied for batch fermentation of bisphenol A degradation in basal salt medium and the half saturation coefficient (KS) and the regression coefficient R2 using Monad, Moser and Tesier kinetic models registered as 9.947 g/L, 12.46 g/L and 14.14 g/L and 0.91, 0.94 and 0.84 respectively. Besides, the utmost specific growth rate mmax was witnessed as 0.841 h1 for the P. aeruginosa (PAb1) regarding BPA degradation. Metabolic intermediates like phenol, acetophenone, and hydroquinone and p-hydroxybenzoic acid were also determined through the degradation process by GC-MS. The metabolic pathway of BPA degradation by the bacterial isolates was also designed in today's analysis. A probabilistic statistical model originated using Box-Behnken response surface methodology and process variables were optimized by nonlinear optimization. © 2017 The Egyptian Society of Radiation Sciences and Applications. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Keywords: Thermal paper industrial effluent Pseudomonas aeruginosa (PAb1) Bio-degradation Bisphenol A Growth kinetic models Statistical optimization

1. Introduction Bisphenol A (BPA) is an organic chemical compound, during polymerization process they produced from phenol and acetone by an acid or alkaline catalyzed condensation reaction hence the suffix name A. BPA is one of the harmful chemical, stated internationally (European Union Risk Assessment Report, 2003 and Nakanishi, Miyamoto & Kawaski, 2005) it is industrially important chemicals key raw materials for the production of polycarbonates, epoxy resins, food products, thermal paper industry and many other products (spivack, Leib, & Lobus, 1994; Kolvenbach et al., 2007;

* Corresponding author. E-mail address: [email protected] (V. Vijayalakshmi). Peer review under responsibility of The Egyptian Society of Radiation Sciences and Applications.

Inoue et al., 2008). Their wide spread consumption of this chemical substance, an significant amount of BPA is directly discharged into terrestrial and aquatic surroundings and becomes an severe toxic to aquatic microorganisms (Alexander, Dill, Smith, Guiney, & Dorn, 1988). BPA is recognized as one of endocrine disruptors (Krishnan, Sathis, Permuth, & Tokes, 1993) it mimic human feminine sexual hormone estrogen. In addition, it is also reported as mutagenic and carcinogenic chemicals (Chai et al., 2005; Kang and Kondo, 2003; Kang, Ri, & Kondo, 2004; Kang, Yshikikatayama, & Kondo, 2006). Bisphenol A can be employed in paper mill industry as a graphic, color programmer and used as antioxidant in the color developing layer of thermal paper documents. Based on the wide application of Bisphenol A, it might be released directly into nearby aquatic systems through manufacturing process and recycling procedure of thermal paper sectors. Microbial biodegradations is a significant

http://dx.doi.org/10.1016/j.jrras.2017.08.003 1687-8507/© 2017 The Egyptian Society of Radiation Sciences and Applications. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

V. Vijayalakshmi et al. / Journal of Radiation Research and Applied Sciences 11 (2018) 56e65

mechanism for reduction of varied environmental pollutants. The significant amount of bacteria's have been isolated from different environmental resources like enrichments of sludge from waste materials treatment plant (Lobos, Leib, & Su, 1992;; Ike, Jin, & Fujita, 2000) aquatic conditions (Miho Sasaki, Maki, Oshiman, Mastumura, & Tsuchido, 2005), soil (Roben & Abeliovich, 2000; danzyl., Sei, Suda, Michihikoike, & Fujita, 2009) and compost leachate (Zhang et al., 2007). There are few number of bisphenol A degrading bacteria system was developed in lab conditions. However, the efficiency of bacterial degradation is low at high concentrations (Tropel & Van der Meer, 2004; Lee, Koo, Choi, Chai, & Jeung, 2005). Hence, the present study was designed to identify the bacterium from thermal paper effluents discharging points of nearby aquatic environments for BPA degradation. In addition an attempt was also made for kinetic analysis of biodegradation using Monod's, Moser and Tesier models, identification of possible metabolic possible metabolic pathway along with intermediates by using Gas chromatography Mass spectroscopic examination, optimization of degradation process variables such as initial concentration of BPA, pH and temperatures by using Box-Behnken response surface strategy and an empirical statistical model. 2. Experimental design 2.1. Reagents, sample collection and media The pure nature of BPA, all microbiological media was purchased from Himedia, Bangalore, India. The reagents and solvents used for GCMS analysis is HPLC grade. The effluent samples were collected from the flow region at 5e10 m depth of effluent discharge point in sterile screw capped bottles, brought to laboratory immediately and stored at 4 C.The Nutrient agar media and Luria Bertani (LB) broth was used for isolation and purification of bacterial species from effluent mixing zone. Basal Salt Mineral (BSM) medium was used for bisphenol A degradation study (Zhang et al., 2007). 2.2. Analysis of physicochemical parameters The Physicochemical parameters like Electrical Conductivity (EC), pH, Temperature (Temp) and Turbidity (TDY) of water samples were analyzed in-situ and expressed by Nephlo-Turbidity Unit (NTU). The other parameters like Dissolved Oxygen, Total Dissolved Solids, Total Suspended Solids, Total Hardness, Total Alkalinity, Ammoniacal nitrogen (NH4N), Nitrite (NO2), Nitrate (NO3), Phosphate (PO4), Chemical Oxygen Demand (COD) Biochemical Oxygen Demand (BOD), Total Kjeldhal nitrogen (TKN), Chloride (Cl-), Sulphate (SO4), Magnesium (Mg), Sodium (Na) and Potassium (Kþ) were analyzed in laboratory after sample preservation, the results were expressed in mg/L, except pH, EC and Temperatures (Khanna, Bhutita, ni, & Matta, 2011); American Public Health Association (APHA, 2005) guidelines. 2.3. Isolation of bisphenol A resistant bacteria The bacterium present in water sample from the effluent disposal site of paper mill industry was enriched in the LuriaBertani (LB) agar plates containing 5 mM of Bisphenol A. The LB agar plates were prepared by dissolving 1 g NaCl, 1 g tryptone, 0.5 g yeast extract, 1.5 g agar and the pH was adjusted to 7e7.2. The medium was sterilized at 121 C for 15 min. The growth of the bacterial colonies was observed after 24 h incubation at 37 C. The morphologically distinct bacterial strains were selected and isolated on nutrient agar medium by repeated streak plate method. The bacterial colonies were screened for their bisphenol A degradation in the LB medium and the best isolates were selected.

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2.4. Identification of bacterial strains The isolated strains were identified using the morphological features like gram staining endospore staining, motility test and 16S ribosomal RNA sequence (16SrRNA) analysis. Genomic DNA isolation was carried out using the standard protocol (Ausubel, 1992). The widespread universal bacterial 16SrRNA gene primers viz Pf: 5IeAGAGTTTGACCTGGCTCAG-3I, and Pr-5I- ACGGCTACCTTGTTACCGACT- 3I, were used for PCR amplification of 16SrRNA gene. Sequences were initially analyzed at National Centre for Biotechnology Information (NCBI) by using Basic Alignment Search Tool (BLAST), corresponding sequences were matched and the phylogenic tree was constructed using Neighbor-Joining method.

2.5. Determination of biomass and growth of Pseudomonas aeruginosa with BPA at different concentrations in basal salt medium The bacterium, Pseudomonas aeruginosa (PAb1) was transferred to basal mineral salt medium (BSM) with varying concentrations (1 Mme35 mM) of BPA. The chemical present in the medium acts as sole carbon and energy source. The bacterial cells were cultivated in Erlenmeyer 500 ml flask added with 100 ml medium, at a temperature of 35 C and the pH was adjusted to 7. The flask was incubated in rotator shaking incubator at 120 rpm. The bacterium Pseudomonas aeruginosa (PAb1) grown in a nutrient medium containing different concentration BPA ranging from 5 mM to 35 mM, after reaching 1 OD, 100 ml of sample withdrawn from the conical flask and centrifuged at 4000 rpm for 20 min. The supernatant was decanted and the pellet was resuspended in to deionized water again re-centrifuged. The supernatant was decanted and pellet was rinsed off in to a preweighed 0.45 mm pore filter paper. The filter paper was dried in hot air oven, cooled in desiccators at room temperature, and reweighed until a constant weight is obtained. The difference between the pre-weighed filter paper and the final constant weight is the Cell Dry Weight (CDW) (Abuhameda, Bayraktar., Mehmetŏglu, & Mehmetŏglu, 2004). 2.6. Microbial growth kinetics of BPA degradation-Theoretical analysis Direct monitoring of the cell morphology and biomass concentrations in culture medium is not easily possible. However, using mathematical model is a suitable way to describe the substrate uptake and cell growth behavior of the microbes (Ardestani, 2011). The values of biomass concentration derived from batch culture of Pseudomonas aeruginosa (PAb1) in nutrient medium was analyzed by application of mathematical models like Monod, Moser and Tesier type kinetic equations in order to determine the kinetic parameters. The specific growth rate (mmax) in exponential phase was calculated using the following equation:

mmax ¼ ln

CDW2 CDW1 t2 t1

(1)

where, mmax - maximum specific growth rate, and t ¼ biomass at different time points (t1 and t2 respectively). The microbial growth kinetics has been described by an empirical model equation (2) actually suggested by Monod, 1942. The Monod model unveiled the idea of a rise limiting substrate. The following equation is needed for the experimental data analysis of growth kinetics.

58

m¼


mmax,S

(2)

Ks þ S

where, m ¼ specific growth rate, mmax ¼ maximum specific growth rate, S ¼ substrate concentration, and Ks ¼ substrate saturation constant (i.e. substrate concentration at half mmax). Moser, (1958) kinetic model is strictly related to substrate concentration. Moser upgraded the model of Monad using a parameter n > 1 to integrate effects of microorganisms adoption to the stationary process. Moser equation is follows as

m ¼ mmax

Sn S þ Ks

(3)

n

where, mmax -maximum specific growth rate, S- Substrate limiting constant, Ks e Moser constant, and n e Fitting constant. Tessier model described the microbial growth and substrate utilization better than the other projected models which is follows

m ¼ mmax 1 eS=Ks

(4)

where, S e Limiting substrate, and Ks- Substrate constant. Regression analysis of the model equations was done by using Non- Linear Regression method of MATLAB. 2.7. Determination of half maximal effective concentration (IC50) The term half maximal effective concentration (IC50) refers to the concentration of a toxicant (BPA) which induces halfway response between the baseline and maximum after a specified exposure time. Generally the half maximal inhibitory concentration (IC50) studied for drugs and metabolites and Endocrine disrupting chemical (EDC) rather than toxicants. BPA was enlisted in EDC chemical by EU assessment reports, (2003). The calculation of IC50 using different concentrations of BPA was plotted against maximum growth rate of Pseudomonas aeruginosa (PAb1). The curve was associated to the Hill model of non-linear regression using the algorithm of Marquard and a non-linear regression program developed by Duggle and described by Fechner, Gourlay, and Uher (2010). The IC50 parameters was calculated by France using Sigma plot software (Okpokwasili & Nweke, 2005). 2.8. Analysis of bisphenol A degradation activity using gas chromatography mass spectroscopy (GC eMS) Pseudomonas aeruginosa (PAb1) was grown in 500 ml mineral medium with 30 mM of BPA. The culture broth of 50 ml was withdrawn after incubation and centrifuged at 10,000 rpm for 15 min. The supernatants were shaken with to 3 50 ml of peroxide-free diethyl ether for 5 min. The pH was adjusted to 9.0 with KOH, and 7.0, 2.0 with HCl. Then the extracts were dried with Na2SO4 and reduced to 0.2 ml by rotary vacuum evaporation (40 C, 850 rpm). The extracts were dissolved in acetonitrile; after by adding 50 ml of N-methyl-N trimethyl silyltrifluor acetamide (MSTFA) the derivative was incubated for 20 min at 70 C. The extract was cooling down to room temperature and analyzed by GC-MS. A Shimadzu QP 2010 plus series Kyoto, Japan, gas chromatograph e mass spectrometer (GC-MS) installed with an RTX5MS column (length, 30 m and inner diameter, 0.25 mm) was used for structure determination (Janett Fischer, Kappelmeyer, Kastner, Schauer, & Heipieper, 2010; Telke et al., 2009). The helium as carrier gas at a flow rate of 1.0 ml/min, an injector temperature is 280 C, oven temperature was initially held at 60 C for 5 min, increased to 180 C for 3 min, further increased to 250 C for

1 min, and finally increased to 280 C for 1 min. The GC-MS interface was maintained at 260 C with 57.4 kpa. Mass spectrum analysis was done at electron energy of 70 eV, the mass charge m/z started at 40 and ended with 400 m/z over a scanning duration of 0.50 s. The structures of the degradation products were confirmed by comparing fragmentation patterns of the mass spectra with those of authentic compounds using NIST library (Fischer et al., 2010). 2.9. Multivariate experimental modeling of bisphenol A degradation The objective of statistical designs is to reduce the number of experiments which is essentially needed for the development and optimization of a process. A common and useful experimental design is one in which all input variables set at twoelevels each. RSM reduces the number of experimental trials required to evaluate multiple variables and their interactions. The BoxeBehnken design (BBD) is widely used RSM (Ponmurugan et al., 2017) for optimization of spherical and revolving design; it consists of a central point and the middle points of the edges of the cube circumscribed on the sphere. It is a second order multivariate technique based on threeelevel fractional factorial design consisting of a full 22 factorial scattered into a balanced incomplete block design. To investigate the behavior of the response surface for the response function (Y) using the secondeorder polynomial equation, a BBD is often preferred since interaction variable estimates are not completely confounded (Ponmurugan et al., 2017). The generalized response surface model is

Y ¼ b0 þ

n n n n pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X X X X b2 4ac bi Xi þ bii Xi2 þ bij Xi Xj þ ε i¼1

i¼1

i

j < i¼1

(5) where, Y is the process response or output, n is the number of the patterns, i and j are the index numbers for pattern, b0 is the free or offset term called intercept term, X1, X2 … Xn are the coded independent variables, bi is the firsteorder [linear] main effect, bii is the quadratic (squared) effect, bij is the interaction effect, and ε is the random error which allows for discrepancies or uncertainties between predicted and measured values. A Box-Behnken design, involving 29 runs was used to determine the effect of the experimental variables considering the percentage removal (%R) as response and the significant variables such as initial concentration of BPA (10e40 mM), pH (4e10), and temperature (20e40 C), previously determined in the screening experiment. Five replicates at the centre of the design were used for estimation of a pure error variance. The usage of Design Expert Statistical Software package 8.0.7.1 (Stat Ease Inc., Minneapolis, USA) was employed for the statistical analysis. 3. Results and discussion 3.1. Physicochemical parameters of effluent mixing point Physicochemical parameters like temperature, hydroxyl ion concentrations (pH) and electrical conductivity of effluent in the discharging point of nearby aquatic bodies were in the range of 29.2e30.4 C, 8.22e8.27, and 293.6e333.6 mS/cm respectively (Table .1). Turbidity, Dissolved Oxygen (DO), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Total hardness and Total alkalinity recorded were in the range of 2.92e3.64 NTU, 5.7e6.2 mg/L, 214e221 mg/L, 3e4 mg/L, 100e110 mg/L, and 87.6e90 mg/L respectively. Whereas, Ammoniacal nitrogen, Nitrite,

V. Vijayalakshmi et al. / Journal of Radiation Research and Applied Sciences 11 (2018) 56e65 Table 1 Physicochemical properties of effluent in mixing point of water bodies. Properties

Unit

Average

Stdev.

Max.

Min.

Temperature pH Electrical Conductivity Dissolved Oxygen Turbidity Total Dissolved Solids Total Suspended Solids Total Hardness Total Alkalinity NH4-N NO3 PO4 Chemical Oxygen Demand Biological Oxygen Demand TKN Chloride Sulphate Calcium Magnesium Sodium Potassium

29.77 8.24 314.37 5.93 3.45 217.72 3.67 104.67 88.73 1.19 12.45 4.33 50.33 27.67 4.39 57.46 18.43 35.47 1.18 48.99 2.67

0.60 0.03 20.04 0.25 0.47 3.27 0.58 5.03 1.21 0.08 0.06 0.09 1.15 1.15 0.15 0.46 0.63 0.69 0.06 1.38 0.21

30.40 8.27 333.60 6.20 3.80 220.52 4.00 110.00 90.00 1.28 12.49 4.42 51.00 29.00 4.56 57.90 18.98 36.26 1.24 49.90 2.90

29.20 8.22 293.60 5.70 2.92 214.13 3.00 100.00 87.60 1.12 12.37 4.24 49.00 27.00 4.28 56.98 17.74 35.06 1.14 47.40 2.50

C e mS/cm mg/l NTU mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l

Nitrate, Phosphate, Chemical Oxygen Demand (COD), Biochemical oxygen demand (BOD), calcium and magnesium of the effluent in the discharging point were recorded in the range of 1.12e1.38 mg/L, 0.00736e0.00917 mg/L, 2.374e2.493 mg/L, 1.236e1.420 mg/L, 49e51 mg/L and 27e29 mg/L, 35.06e36.27 mg/L and 1.14e1.24 mg/ L respectively. However, total Kjeldhal Nitrogen, Chloride, Sulphate, Sodium and Potassium of the effluent in the discharging point were recorded in the range of 4.28e4.56 mg/L and 46.98e49.9 mg/L, 17.7e19.0 mg/L, 39.68e45.9 mg/L and 2.5e2.9 mg/L respectively (Table .1). The results of physicochemical parameters of water samples showed that the values were significantly higher than permissible limits. In account for pollution of the river water by mixing of industrial effluents, similar kind of results were reported by many researchers (Khanna et al., 2011; Matta, Srivastava, Pandey, & Saini , 2016; Saradhamani, Shanthi, Kumaravelu, & Jaganatahn, 2002; WHO, 1990). 3.2. Morphological and 16SrRNA based identification The isolated bacterium was rod shaped, gram negative and motile. The isolated organisms belongs to genus Pseudomonas aeruginosa (PAb1) (99% Similarity with species) on the basis of 16SrRNA gene sequences analyzes of 736bp gene and the gene sequences was deposited in the Gene Bank database under the Accession Number JN674083.1. The phylogenic tree was constructed based on 16SrRNA gene sequence and comparison with 736bp shows the relationship between different members of genus and our bacterial isolate namely BPADEG (Fig. 1). Similar kind of gene sequence analysis for identification of bacteria against degradation was reported by many authors (Zhang et al., 2007; Telke et al., 2009). 3.3. Bisphenol A degradation and calculation of maximum specific growth rate (mmax) of bacterial isolate The bacterial isolate (Pseudomonas aeruginosa (PAb1) was streaked in basal slat mineral medium with various concentrations of BPA ranged from 5 mM to 35 mM. The organism able to degrade BPA up to 35 mM and the presence of growth in basal salt mineral medium confirmed the BPA degrading activity. However, no growth of bacterial isolate was observed above 35 mM BPA concentration. The maximum specific growth rate (mmax) of bacterial isolate in various BPA concentrations such as 5 mM, 10 mM, 15 mM, 20 mM,

59

25 mM, 30 mM and 35 mM were found as 0.842, 0.782, 0.712, 0.621, 0.576, 0.479 and 0.431 mmax (h1) respectively (Fig. 2). The results revealed that the Pseudomonas aeruginosa able to degrade high concentrations of (35 mM) of BPA in BSM medium. A novel bacteria Achromobacter xylosoxidans (strain B-16) was isolated from the compost leachate of the Municipal Solid Waste (MSW) in a laboratory reactor. The bacterial strain utilizes 5 mg/L of BPA as a sole carbon source under aerobic condition (Zhang et al., 2007). 3.4. Biomass growth of Pseudomonas aeruginosa (PAb1) at different concentrations of BPA in basal salt medium The biomass growth of isolates at different concentrations of BPA in nutrient agar medium was evaluated. The maximum amount of biomass growth was observed in nutrient medium enriched with BPA at 5 mM (0.384 g/L) followed by 10 mM (0.326 g/L). The other concentrations like, 15 mM, 25 mM, 30 mM and 35 mM have registered less amount of biomass growth (Fig. 3). The results show that increase of BPA concentrations and decreasing of biomass concentration which reflected the toxicity of BPA against the Pseudomonas aeruginosa (PAb1). Similar kind of results was reported by (Mohebali, Ball, Rasekh, & Kaytash, 2007). 3.5. Determination of half maximal effective concentration (IC50) for BPA degradation by Pseudomonas aeruginosa The half maximal inhibitory concentration (IC50) of bacterial isolates was studied for BPA degradation in basal salt mineral medium. The effective IC50 value in nutrient medium for isolated bacterial strain (Pseudomonas aeruginosa (PAb1)) was recorded as 32 mM. European Union Risk Assessment, 2003 & 2005 revealed similar type of results in fresh water algae and other organisms. 3.6. Growth kinetics of Pseudomonas aeruginosa (PAb1) for bisphenol A degradation To determine the growth kinetic parameters, specific growth rates (m) of the culture at different bisphenol A concentration were calculated as per the following relationship:

m¼

1 dX X dt

(6)

where, X is biomass concentration (g/L) at time, t (h) and m is the specific growth rate (h1) that is calculated from the slope of linear exponential growth curve versus time (gL1 h1), dividing the average cell mass concentration (g/L). Half-saturation coefficient (Ks) and regression co efficient (R2) were determined for Monod, Tessier and Moser models and the values were recorded as 9.947 g.l1, 12.46 g.l1 and 14.14 g.l1 respectively. The regression co efficient values of the above models were calculated as 0.91, 0.94 and 0.84respectively. The maximum specific growth rate (mmax) 0.841 h1 was observed for all kinetic models. Thus, Monod, Tessier and Moser models were able to describe cell growth and nutrient (Bisphenol A) uptake kinetic of Pseudomonas aeruginosa (PAb1) in nutrient medium. The regression co efficient (R2) is frequently used to judge whether the model represents correct data, if implying the regression co efficient (R2) that is close to one then the model is fit (Tropel & Van der Meer; 2004; Mohebali et al., 2007; Annuar, Tan, Ibrahim, & Ramachandran, 2008; Ardestani, 2011). The Tessier model regression co efficient (R2) of the present study was calculated as 0.94, so the kinetic parameters and regression co efficient revealed that, the Tessier model is the best suited model compared to Monod and Moser for explain growth kinetics of bacterial isolates (Table 2). Annuar et al. (2008) have reported similar kind of


Fig. 1. Phylogenic tree of Pseudomonas aeruginosa.

60


61

Fig. 2. Specific growth rate value of Pseudomonas aeruginosa in Basal salt medium.

Fig. 3. Biomass growth of Pseudomonas aeruginosa in basal salt mineral medium.

growth kinetics in Pseudomonas putida. 3.7. Analysis of BPA metabolites using GC-MS In order to elucidate bisphenol A degradation pattern of Pseudomonas aeruginosa a detailed GC MS spectrum was carried out. This analysis reveals that presence of four different metabolites viz., namely phenol (8.505), Acetophenone (10.68), hydroquinone (14.868), p-hydroxybenzoic acid (18.992) and bisphenol (28.378) (Table 3). The similar kinds of observations were made by many researchers (Lobos et al., 1992; Fischer et al., 2010; Mahanta, Borah, & Pallabi Saikiaet al., 2014). 3.8. Identification of BPA metabolites The GCMS analysis showed the metabolites could be definitely Table 2 Kinetic parameters of Pseudomonas aeruginosaobtained for BPA degradation. MODELS

KS

I

R2

Monad model Tessier model Moser model

9.947 12.46 14.74

e 6.289 9.564

0.89 0.95 0.84

identified as concern compound by comparisons with known authentic compound. The mþ ion is found at m/z94 for phenol. The molecular weight of the phenol is 94. The base peak value of phenol was observed as 94 and mass peak values were recorded as 66, 39, and 55. The mþ ion for acetophenone is m/z 120. The molecular weight of the compound was observed as 120, the base peak and mass peak values were recorded as 105 and 91, 77, 51, 39, and 27 respectively. The hydroquinone was identified at m/z 110. The molecular weight of the compound hydroquinone was observed as 110. The base peak and mass peak values of hydroquinone were recorded as 110 and 110, 81, 69, 63, 55, 51, 53, 39, 27, and 28 respectively. The mþ ion at m/z 138 was identified as hydroxyl benzoic acid. The molecular weight of the above compound was recorded as 138. The base peak and mass peak values were 121 and 138, 110, 93, 8, 74, 65, 63, 53, 45 and 39 respectively. However the mþ ion for BPA was found at m/z 228, the base peak and mass peak values were 213 and 197, 135, 119, 107, 91, 77, 65 and 39 respectively. The bacterium Pseudomonas aeruginosa (PAb1) involves different kind of metabolic pathway during bisphenol degradation. The first rung on the ladder, pathway I is carbon -carbon cleavage occurs on right side of bisphenol ring leading to development of phenol. Exactly the same carbon -carbon cleavage happen on left area of bisphenol resulting in development of 4 hydroxy-isopropenyl benzene, after there is deoxygenating reaction take place on 4-

62


Table 3 Mass spectrum analysis of Pseudomonas aeruginosa produced bisphenol degradation products. Metabolites

Retention time (min)

Chemical structure/MW (m/z)

Phenol

8.505

C6H6O/94

Acetophenone

10.68

C8H8O/105

Hydroquinone

14.868

C6H6O2/110

p-hydroxybenzoic acid

18.992

C7H6O3/121

Bisphenol

28.378

C15H16O2/213

hydroxy - isopropenylbenzeneresulting in development of isopropenyl benzene. Both of these compounds not recognized in present study however the bisphenol undergone this way of degradation reactions. These results have a good consistence with those reported by Zhang et al. (2007); Fischer et al. (2010). Furthermore, the oxidative reaction of isopropenyl benzene brings about development of acetophenone. The other systems of metabolic pathway II is 4-hydroxy - isopropenyl benzene acted as precursors for the forming of p-hydroxybenzoic acid. Pathway III is the original carbon -carbon cleavage on bisphenol accompanied by oxidation causes the forming of hydroquinone (Fig. 4). The similar kind of degradation pathway by bacterial isolates were reported (Zhang et al., 2007; Fischer et al., 2010). The bacterium Pseudomonas aeruginosa (PAb1) mineralize BPA in substitute pathway, similar kind of substitute pathway in the bacterium, Cupriavidus basilensis JF1 was reported (Taeko Hirano Honoda, Watnabe, & Kuwahara, 2000; Zhang et al., 2007; Fischer et al., 2010). In the end of degradation activity, all the metabolites undergone mineralization process and turned into simpler substances by the influence of microflora. Similar kind of results were reported by many researchers (Miho Sasaki et al., 2005; Kolvenbach et al., 2007; Zhang et al., 2007; Telke et al., 2009; Fischer et al., 2010).

3.9. Box Behnken analysis The optimal values of selected variables were obtained by resolving the regression equation (Ponmurugan et al., 2017; Sivarajasekar, Mohanraj, Baskar, & Sivamani, 2017a, pp. 1e11) in order to get a polynomial model (Eq. (4)) via BoxeBehnken design.

Mass spectrum

The specified ranges of variables are shown in Table 4. The obtained predicted values (%Rpred) were well correlated with the experiential values (%Ract) with low error percentage ( F-values < 0.0001 shows that the model is important, whereas values larger than 0.05 shows that the model is not important under certain situations. An insignificant p value (p > F < 0.0001) and F-value of 1491.34for the quadratic model specified that the model was important for BPA degradation. Model summary statistics indicated that, quadratic model had reasonable “Adjusted R2” (R2adj) and the “Predicted R2” (R2pred) values, indicating a good correlation between the %Rpred and %Ract (Alqadami et al., 2017; Padmanaban, Giri Nandagopal, Anant Achary, Vasudevan, &


63

Fig. 4. Metabolic pathway and degradation mechanisms of bisphenol by Pseudomonas aeruginosa (PAb1). Table 4 Box Behnken Design matrix with experimental and calculated %R values.

Table 5 ANOVA table for BPA degradation by Pseudomonas aeruginosa.

BPA initial concentration (X1, mM)

pH (X2)

Temperature (X3, C)

%Rexp

%Rcalc

Source

Sum of Squares

df

Mean Square

F value

p-value Prob > F

10 40 10 40 10 40 10 40 25 25 25 25 25 25 25 25 25

4 4 10 10 7 7 7 7 4 10 4 10 7 7 7 7 7

30 30 30 30 20 20 40 40 20 20 40 40 30 30 30 30 30

85.99 76.26 97.35 87.14 83.46 74.12 100 90.16 60.17 68.78 75.52 88.89 95.77 95.77 95.77 95.77 95.77

85.93 76.39 97.22 87.20 83.20 73.67 100.46 90.43 60.50 69.17 75.13 88.56 95.77 95.77 95.77 95.77 95.77

Model X1 X2 X3 X1 X2 X1 X3 X2 X3 X21 X22 X23 Residual Lack of Fit Pure Error Cor Total Std. Dev. Mean Adequate Precision

2142.63 191.29 244.42 578.68 0.0576 0.06 5.66 21.41 541.45 517.84 1.11 1.12 0 2143.75 0.39954 86.2759 0.4631

9 1 1 1 1 1 1 1 1 1 7 3 4 16 R2 R2adj PRESS

238.07 191.29 244.42 578.68 0.06 0.06 5.66 21.41 541.46 517.85 0.16 0.37 0

1491.34 1198.33 1531.15 3625 0.36 0.39 35.48 134.12 3391.82 3243.91