Decolorization of Textile Dye by Brevibacillus laterosporus (TS5) and Influencing Factors Optimization through Response Surface Methodology

The dye removal bacteria Brevibacillus laterosporus (TS5) was isolated from dye contaminated soil, and it’s identified by 16S rDNA sequencing method. The prospective bacterial strain exhibited a highest decolorization (97.8%) in Luria-Bertani broth medium. Among the operational factors, Plackett-Burman design, experimental results indicated that pH, incubation period, and yeast extract significantly contributed for the dye decolorization. Also, dye concentration, starch, temperature, and inoculum size noted as insignificant factors on dye decolorization. Central composite design applied for optimization of important factors to enhance the dye decolorization by Brevibacillus laterosporus (TS5). The optimal values of significant factors were determined by the Response surface methodology (RSM) as follows: 0.60% (w/v) yeast extract, 7.23 pH and 61.45 hrs incubation period, which assisted for Brevibacillus laterosporus (TS5) to attain 90.66% dye removal. Brevibacillus laterosporus (TS5) showed 90.08% decolorization in validation experiments by the support of optimal factors, and implies that explored strain could be a suitable candidate for bioremediation of dye containing effluents. Archives of Ecotoxicology, Vol. 2, No. 3, pp. 51-60, 2020


Introduction
The disposal of waste from textile industries is considered as major environmental problem in the worldwide. Particularly, the discharging of textile industry effluent into the ecosystem is hazardous one, as it contains bio-recalcitrant dye stuffs The most important cultural factors are carbon source, nitrogen source, dye concentration, pH, inoculum size, temperature, and the incubation period is dynamically affected the efficiency of microbial activity and decolorization processes (Garg et al. 2017). Consequently, their relevant factor optimization is prerequisite one, prior to real effluent studies, to avoid any inhibitory effect on dye degradation process. Therefore, the necessity found in the screening of robust bacteria which are able to adopt, and decolorize the textile effluent in an adverse condition. Also, key factors optimization by response surface methodology (RSM) approach can enhance the performances of bacterial dye decolorization and its effectiveness (Mohana et al.

2008).
Mostly statistical tools employed to simplify the operations, chemical consumption, and the time involved in a process. Moreover, RSM is an efficient chemometric empirical approach that can be used to eliminate insignificant variables, and to study the relationship between a set of significant factors in optimized 52 process (Chen, 1994). In this view, the present study focused on the exploration of textile dye decolorizing bacteria Brevibacillus laterosporus (TS5) isolated from the dye contaminated soil, and its dye decolorization efficacy was enhanced via optimization by screening of influencing factors in dye removal process with Plackett-Burman design, followed by application of response surface methodology with central composite design.

Screening of dye decolorizing bacteria
The dye contaminated soil sample was collected from a textile dyeing industry located in Tirupur district of Tamil Nadu, India. The bacterial colonies were isolated and enumerated by nutrient agar medium (pH 7.0±0.2) containing, 5 (g/l) Peptone, 5 (g/l) Sodium chloride, 3 (g/l) Yeast extract, 20 (g/l) Agar. The pour plate method employed plates were incubated at 35±2 °C, and 45±2 °C for 24 -48 hrs incubation. The decolorizing ability of bacterial colonies was determined in Luria Bertani agar plates containing, 10 (g/l) Casein enzymic hydrolysate, 5 (g/l) Yeast extract, 10 (g/l) NaCl, 15 (g/l) Agar, amended with various concentrations (50, 100, 150, 200 and 250 mg/l) of remazol golden yellow (RNL) dye, and incubated at 37 °C for 4 days. The bacterial colonies which showing clear zones on the agar plates were selected for decolorization studies.

Identification of dye decolorizing bacteria
The dye decolorizing bacterial strain TS5 was identified by 16S rDNA sequencing method. The genomic DNA isolation, extraction, PCR amplification, and 16S rDNA sequencing was carried out in Xcelris Labs Ltd, Ahmedabad, India. The generated 16S rDNA sequences were compared to sequences of the NCBI server using BLASTN tool. The closely related sequences were aligned with multiple alignment program Clustal W. Phylogenetic tree was constructed by the neighbour-joining (NJ) method in MEGA version 5. The sequences have been deposited in NCBI GenBank (http://www.ncbi.nlm.nih.gov) under accession number JQ885974.

Effect of nutritional substrates in dye decolorization
The effect of co-substrates on dye decolorization using Brevibacillus laterosporus (TS5) was carried out in the various broth compositions (100 ml each) Luria Bertani broth (g l -1 ): Casein enzymic hydrolysate 10.0, yeast extract 5.0, NaCl 10.0; Yeast extract broth (g l -1 ): yeast extract 5.0, NaCl 5.0; Bushnell and Hass broth (g l -1 ): MgSO4 0.2, K2HPO4 1.0, CaCl2 0.02, FeCl3 0.05, NH4NO3 1.0 (remazol golden yellow dye concentration, 100 mg/l). About 1 ml of inoculum with an optical density of 1.0 was inoculated. Experimental flasks were incubated statically at 37 °C for 3 days with abiotic control. The samples were withdrawn at 24 hrs intervals, and centrifuged at 5000 rpm for 20 min. The absorbance value of the cell free supernatant was analyzed in UV spectrophotometer (Cyberlab UV-100 USA) at 412 nm. Triplicate experiments were performed in simultaneously, and decolorization percentage was calculated using the Equation (1).

Equation (2)
Where Y is the response (Percentage of dye decolorization), Xi is factor levels, i is factor number, β0 is the model intercepts term, βi is the linear effect, βii is the squared effect, βij is the interaction effect between Xi and Xj on dye decolorization process. As per the design, experiments were carried out in flask culture under static incubation. All the runs were performed in triplicate, and the averages of decolorization percentage were considered as the response. Statistical analysis of the Plackett-Burman design results were performed by using statistical software Minitab Version 15.0.

Response surface methodology -Optimization of dye decolorization
The concentrations of yeast extract, incubation period, and pH were the significant factors for effective dye decolorization was identified from results of Plackett-Burman design. Response surface methodology was used to study the optimal region of significant factors, and their interactions on remazol golden yellow dye decolorization using strain Brevibacillus laterosporus (TS5), a full factorial central composite design was employed. Each significant factor was studied at five levels (-α, -1, 0, +1 and +α) and their actual values are shown in Table 3. Where α = 2 n/3 ; here "n" corresponds to the number of factors and "0" corresponds to the central point. The Equation (3) was adopted to calculate the actual values of significant factors. The experimental model of central composite design was given in Table 4. The other cultural factors were maintained in constant range, such as, sodium chloride 0.5% (w/v), starch 0.55% (w/v), dye concentration 200 mg/l, inoculum size 7.5% (v/v), and temperature 37 °C. The experimental results obtained from the central composite design were established in second order polynomial model Equation (4) for the prediction of decolorization efficiency, and the significant factor interactions within testing range.

Equation (4)
Where Y is the response, β0 is the intercept term, β1, β2 and β3 are linear coefficient of tested factors, β11, β22, β33 are quadratic coefficient, β12, β13, β23 are interaction coefficient, and X1, X2 , X3, X4 are coded factors. All the experiments were carried out in triplicate, and the statistical software package Minitab Version 15.0 was used to mathematical interpretation of experimental results. The significant factors optimal value was established from Central composite design results with response surface analyzer. Under optimal values of these key factors, the validation study was performed, and enhancement of dye decolorization efficiency was evaluated by comparing the experimental results with predicted values

Screening of significant factors on dye decolorization
The influence of operational factors in remazol golden yellow decolorization by Brevibacillus laterosporus (TS5) was scrutinized in Plackett-Burman design and the results are ranging from 0.68 to 53.69% (Table 5), it reflected the significant effect of factors in dye removal process. The diverse culture conditions were responsible for the decolorization performance of Brevibacillus laterosporus was denoted by Gomare et al. (2009c). Standardized effects in a pareto chart (Figure 2) illustrates the significant factors of dye decolorization, which was yeast extract, pH and incubation period respectively. Analysis of estimated effects and regression coefficient represents the factors significance level ( Table 6).
The coefficient value of yeast extract, pH, and incubation period indicates the positive effect accumulated via increased their concentration. The availability of carbon/nitrogen sources determines the rate of dye decolorization, since it acts as an    ANOVA of linear model explains the factors affecting on remazol golden yellow decolorization ( Table 7). The fisher's F -test value of the response is 7.69 and its delivered that the model was significant one, the value of Probability (P) > F is less than 0.034. The operational factors P-value were less than 0.10 indicates, that the model and those factors are highly significant.

Optimization of dye decolorization
From the results of Plackett-Burman design, yeast extract, pH and incubation period found as the most important factors and their levels were optimized in response surface methodology (RSM) for maximum decolorization of remazol golden yellow by Brevibacillus laterosporus (TS5). The experimental results of % dye decolorization involving central composite design were varied from 5.62 to 87.15% (Table 8).  The decolorization result (%) was fitted with second order polynomial model equation (6) to explain the confidence of dye removal percentage. The estimated regression coefficient for the model was given in Table 9. The small p-value corresponding to larger t-value explains that the factors and model term are significant. Also, correlation coefficient (R 2 ) was found to be 99.18% that reveals the presence of a good correlation between studied and predicted decolorization (% Where Y is response (percentage of dye decolorization), X1, X2 and X3 were the coded values of yeast extract, pH and incubation period respectively. In analysis of variance determined data (Table 10), the calculated F-value = 134.59 and probability value P=0 shows the model as a significant one. The enhanced effects, linear (p=0.000), quadratic (p=0.000) and interaction (p=0.006) are found between the factors in dye removal. The significant factors interaction and their effects on dye decolorization were graphically shown in response contour plots (Figure 3a, b, c).   reported that low decolorization efficiency of bacteria at the end of 21 days incubation, which was 20% for direct yellow and 25% for red dyes (10 mg/l). Since, the toxic nature of dye intermediates, and their concentration varied among dyes inhibits the growth of bacteria, the subsequently reduced decolorization occurred. These are the findings denotes the operational factors influence, and significant factor optimized levels enhances the decolorization rate of Brevibacillus laterosporus (TS5).

Conclusion
The present study demonstrated that the decolorization potentiality, and statistical optimization of bacterium Brevibacillus laterosporus (TS5) for an effective dye decolorization process. The precise ranges of various operational factors affect the dye decolorization rate of bacteria. Plackett-Burman design approach eliminated the insignificant factors from multiple factors of the optimization process. In these, the contribution of starch seems to be less effective in promoting decolorization. However, decolorization augmented with the addition of yeast extract as a nitrogen source. It indicates the bacterial cells, either utilized dye as carbon source instead of added starch. The bacteria showed a maximum dye removal capacity with neutral pH highlights their positive interaction of dye molecules. The robust dye decolorization obtained with assistance of significant factors, optimal level, and confirms the important factors interactions on decolorization. Hence, this result suggested that the isolated bacteria and their optimized condition could be an effective role in the removal of dyes from textile waste water treatment system.