Optimization of Lipase Production with Response Surface Method using Bacillus subtilis.
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<h1>Optimization of Lipase Production with Response Surface Method using Bacillus subtilis</h1>
<h2>Int. J. Micro. Sci. 2024; 5(1), 30-38</h2>
<p><strong>Authors:</strong> Suyash Dusane, Sudhakar Gutte</p>
<h2>1. Introduction</h2>
<p>Lipases (triacylglycerol acyl hydrolases, EC 3.1.1.3) are widespread enzymes present in animals, plants, fungi, and bacteria. They play a crucial physiological role by catalyzing the hydrolysis of triacyl glycerides into glycerides, monoglycerides, glycerol, and fatty acids at the lipid-water interface [1].</p>
<p>Lipases are highly versatile biocatalysts capable of facilitating a variety of bioconversion reactions, including hydrolysis, interesterification, esterification, alcoholysis, acidolysis, and aminolysis. Due to the catalytic abilities of hydrolyzing triglycerides, esterification, trans-esterification, and enantio-separation, triacylglycerol lipase (EC 3.1.1.3) finds widespread applications across industries such as detergents, pharmaceuticals, food (cheese and tea), pulp and paper, textiles, tanneries, cosmetics, biodiesel production, wastewater treatment, medicine, food, chemistry, textiles, and related industries [2-4].</p>
<p>Microbial lipases are particularly notable for their extensive industrial applications, ease of cultivation, and scalability in production [5, 6]. Microbial lipases are primarily produced through submerged fermentation (SmF), a well-established method with fully developed engineering aspects. However, solid-state fermentation (SSF) has demonstrated certain advantages over SmF for enzyme production, even on a commercial scale [4].</p>
<p>The traditional approach of optimizing media for the fermentation process by varying one variable at a time was often time-consuming, costly, and likely to misinterpretation. However, the high production costs of biocatalysts frequently limit their widespread application [7].</p>
<p>To address this challenge, researchers explore various microorganisms, supplements, and substrates to identify the most effective combinations for producing high-value lipases. This approach focuses on using substrates and operational conditions that can lower production costs on an industrial scale [8].</p>
<p>To enhance microbial lipase production, various culture parameters are typically examined, including carbon sources, nitrogen sources, initial pH, temperature, and aeration conditions [9]. The high cost of carbon sources contributes to the expense of lipase production. To mitigate this, low-cost substrates such as molasses [10], glycerol [11], olive pomace, and wheat bran [12] have been explored as viable alternatives. Edible triacyl glycerides, including olive oil, palm oil, sunflower oil, and soybean oil, have also been used as carbon sources and inducers in lipase production [13].</p>
<p>Optimization of media components for every possible combination of required carbon, nitrogen sources, and inducers was impractical due to the large number of required trials. In contrast, statistical experimental methods provide a more efficient and cost-effective way to simultaneously and systematically vary all components [14].</p>
<p>Response Surface Methodology (RSM) is an experimental approach used to determine the optimal conditions in a multivariable system. It is particularly effective for optimizing significant variables identified through factorial designs, such as the central composite design (CCD). By integrating factorial design and regression analysis, RSM evaluates the impact of various factors and their interactions. Advanced applications of RSM involve mathematical models to analyze experimental data and predict relationships between responses and variables, enabling the creation of predictive contours that facilitate faster and more efficient optimization with fewer experiments [15].</p>
<p>The main purpose of this study was to optimize the media components for cost-effective extracellular lipase production using soil microorganisms. Utilizing the statistical optimization tool Response Surface Methodology (RSM), the study was conducted to enhance the efficiency and yield of lipase production. By systematically analyzing and refining the conditions, this approach aims to identify the optimal parameters that will maximize enzyme output and improve overall production processes.</p>
<h2>2. Materials and Method</h2>
<h3>2.1 Sample collection</h3>
<p>Soil sample was collected from agricultural fields from the Aurangabad district, at a depth of 5-6 cm. Using a sterile spatula, samples were placed in sterile 50 ml tubes to avoid contamination.</p>
<h3>2.2 Isolation and screening</h3>
<p>For serial dilution, 1 g of soil sample was mixed with 10 ml of sterile distilled water in a 50 ml Erlenmeyer flask, then agitated at 120 rpm for 30 minutes at 37°C using a rotary shaker. The aqueous slurry was serially diluted up to 10⁻⁶ using 0.8% saline. Then, 100 µl from each dilution was spread on tributyrin agar plates containing 0.5% peptone, 0.3% yeast extract, 1% tributyrin, and 2% agar. The plates were incubated at 37°C for 24-72 hours, and lipolytic activity was assessed by observing zones of hydrolysis around bacterial colonies. The isolates showing maximum zone of clearance were selected for further analysis [16].</p>
<h3>2.3 Morphological, Biochemical and Molecular identification of the isolates</h3>
<p>The morphological and biochemical characterization of lipase-producing bacterial isolates was conducted following guidelines from Bergey’s Manual of Systematic Bacteriology [17]. The confirmation of the isolates was done by molecular identification using 16s rDNA sequencing.</p>
<h3>2.4 Response Surface Methodology</h3>
<p>To determine the optimal media component for lipase production from the isolated bacteria, molasses (g/L), peptone (g/L), and palm oil (%v/v) were selected as components of the culture medium. Central Composite Design (CCD) was then utilized to optimize the concentrations of these components and to evaluate their individual effects and interactions on lipase yield [18]. The experimental setup included a three-factor, five-level design (−α, −1, 0, +1, +α) with 20 experimental runs, detailed in Table 1. To develop the quadratic model and analyze the multinomial coefficients, experiments were conducted using Design Expert v11.1.2.0 software, with lipase production measured in U/mL as the response variable (R1) [19].</p>
<h3>2.5 Statistical analysis</h3>
<p>Statistical analysis included the evaluation of the correlation coefficient (R), multiple correlation coefficient (R²) representing the fit of the quadratic model, and Fischer’s F-statistic along with its probability p(F). Response surface curves were generated based on the quadratic model [20]. The analysis was done using Statease Design Expert version 7.</p>
<h2>3. Result</h2>
<h3>3.1 Isolation and screening</h3>
<p>Colonies exhibiting maximum zone of clearance were selected for quantitative measurement of lipase activity, as colonies with a larger zone of clearance typically indicate higher enzyme activity. Among the isolates, one strain BS-L1 demonstrated the highest zone of clearance and was thus used for further identification and enzyme production studies.</p>
<h3>3.2 Identification of the isolate</h3>
<p>The isolated strain was gram-positive in nature. The selected lipolytic isolate was identified using sequencing techniques, PCR amplification of the 16S rDNA gene. The resulting sequence was then compared with those in GenBank using the BLAST program. As per the results, the isolated bacteria were identified as Bacillus subtilis.</p>
<h3>3.3 Optimization of the media component by Response Surface Methodology</h3>
<p>The experimental design focused on three variables: molasses, peptone, and palm oil, each tested at specific levels for lipase production using the CCD model. Molasses was assessed within a range of 2 to 18 g/L, with the extreme values of −α and +α set at -3.45434 and 23.45434 g/L, respectively. Peptone concentrations varied from 3 to 27 g/L, with −α and +α levels extending from -5.18151 to 35.18151 g/L. Palm oil was evaluated at concentrations ranging from 0.1% to 0.7% v/v, with −α and +α levels adjusted to -0.10454% and 0.904538%, respectively.</p>
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<h2>4. Discussion</h2>
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<h2>5. Conclusion</h2>
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