Process Optimization for Biosynthesis of Pyruvate Decarboxylase (PDC) and Neuberg's Ketol (PAC) from Novel Pichia Cecembences Through Response Surface Methodology using Industrial Waste as Substrate


 Purpose Phenyl acetyl carbinol (PAC) is an intermediate for the synthesis of active pharmaceutical ingredients(ephedrine, pseudoephedrine, norephedrine etc.)which are used for the production of antiasthematics and decongestants. Chemical production of these APIsand extraction from plantis costly and cumbersome. Biosynthesis ofPACthrough condensation of benzaldehyde and acetaldehyde using PDC, amore effective method, is being used. These solvents can adversely inhibit PDC. Optimization of cointeraction of significant factors was done through Response surface methodology (RSM) in relatively short time.Method The effect of incubation time (8-18hrs), temperature (30-38oC), pH (4-10) and Inoculum size (4-10%,v/v)on PAC yield, sugar consumption and PDC activity was determined.PAC was quantifiedSpactrophotometerically and HPLC.All results and models were statistically analysed. PDC,produced in 5L flask using molasses as substrate, was exposed to 40mM benzaldehyde as whole cells, crude extract and partialy purified to determine its half life as residuel activity.Results PDC activity and PAC yield were 56.27 U/ml and 8.44 g/L, respectively. The yield of PAC(2.22 to 8.44g/L) was increased by 71% after process optimization through RSM with time (13hrs), temperature(33°C) and total sugar conc. (18%,v/v) as significant factors (p-values, 0.902, 0.260 and 0.247, respectively). Process design had Adj R2 0.562, R-Squared 0.770, Adeq Precision 4.888 with a uniformly distributed standard error.PDC used in the form of Pichia cecembences cells revealed higher stability towards benzaldehyde and temperature as compared to partially purified PDC.Whole cells and partially purified PDC showed half-lives of 240 and 72hrs at 4oC whereas, 33 and 28.5hrs at 25oC. PAC,purity though HPLC was 76.18%. Conclusions Time, temperature and sugar were significant factors as they increased the PAC biosynthesis.PDC from Pichia cecembences(crabtree negative;reported in other publication by same authors), as a whole cell and purified showed better half-lives at 4 and 25oC as compared to reported PDCs.Hence, it is a promising candidate for commercial production of PAC, as its PDC was stable at 4 and 25oC in presence of Benzaldehyde.

two types of properties; Decarboxylation and Carboligation, through decarboxylation of pyruvate, it produces acetaldehyde and carbon dioxide (Nichols et al., 2003) whereas incarboligation it adds carbon from Benzladehyde into the acetaldehyde resulting in the production of a large number of products like phenyl acetyl carbinol (PAC), benzyl alcohol, benzoic acid, formic acid, acetaldehyde and other side products (Suresh et al.2009, Meyer et al. 2010, Thitipraser et al. 2014).Enzymes, involved in Decarboxylation (a critical reaction in fermentation process)including PDC are thymidine Phosphate (ThdP) dependent enzymes andbelong toLigases (Nichols et al. 2003, Andrews and McLeish2012).
PAC is an intermediate compound in the production of Active Pharmaceutical Ingredients (API)i.e., norepherine, L-ephedrine, pseudoephedrine, nor-ephedrine, nor-pseudoephedrine etc. Alternatively, Ephedrine being an important anti asthmatic drug is traditionally extracted fromEphedra (Ephedra sinica) which is not abundantly found in most of the parts of the world.In addition, they are not easy to cultivate and the extraction procedures are cumbersome (Abourashed et al. 2003 PDC can be produced from yeasts (Saccromyces cerevesae, Candidautilis etc.) and bacteria (Zymonas mobilis) through fermentation but yeast PDC is preferable over bacterial PDC because bacterial PDC are more sensitive towards benzaldehyde and lose their productivity after a short time of exposure. Therefore, yeasts are the most suitable sources of PDC due to this and number of other factors (Raj KC et al. 2002).
Fermentation process is affected by many physical and chemical parameters so theconventional optimization of fermentation(varying one parameter at a time) is a time consuming and hectic practice.Moreover, interaction among variables is not considered in such practices. If interaction among the factors is ignored it is di cult to reach optimum factors with best interactions (Mason et al. 2003).
Therefore, in present study, a statistical method viz., Response Surface Methodology (RSM) is used to study in uences of individual factors and their interactions. RSM is a statistical tool for experiment design, screening of factors to select signi cant factor (through linear andco-relation regression analysis) and selection of the best in uence of the signi cant factors over product formation (Mason et al. 2003, Mushtaq et al. 2014). RSM has been usedby many researchers as it is an e cient statistical tool for process design and developmentof methods to produce many valuable biomolecules (Mushtaq et al. 2014, Othman et al. 2017).
In present study, initial experiments were designed through PBM via linear regression analysis to select signi cant factors from physical and chemical factorsi.e., Time of fermentation (Hrs), incubation temperature ( o C) for fermentation, inoculum size (%, v/v), pH of medium,conc. of sugar (%,v/v), urea (%,w/v), MgSO 4 (%,w/v) and TPP (%,w/v). Central composite design was used for co-linear regression analysis. Stability of PDC as whole cells, crude extract and partially puri ed was evaluated.

Results And Discussion
Selection of correct carbon source is very critical in process optimization.Carbon sources were screened for the production of PDC and biosynthesis of PAC. It was observed that cane molasses was the bestcarbon source as it produced 2.22 g/L PAC whereas other sources produced lesser amounts.That ispure cheese whey (whey without suspended proteins) 1.11, whole cheese whey (whey with suspended proteins) produced 0.88, glucose 0.99, galactose 0.44, fructose 0.66, maltose 0.77 and Carbon sulphite liquor 0.33 ( Fig. 1.Screening of carbon sources for PDC production.).Some other researchers stated thatcane molasses as an excellent carbon sourcefor growth of yeastsand production of different enzymes and other metabolitessuch as pectinases proteases, xylanases and bioethanol etc. Other factors were non-signi cant as R 2 was greater than 1 and P value was out of standard limit.Linear regression analysis of eleven factorsthrough four responses (Table 1A)was done using Plackett Burman Model.Standard error of design ( Fig.2) was smaller and similar within all types of co-e cients. VIF was 1.0 and hence satisfactory. Ri 2 for all factors was 0.00. All these aspects showed terms are not co-related and Model is signi cant.
As per ANOVA, P-values for initial sugar (%,v/v), Time (Hrs)and Temperature ( o C) were less than 0.05 therefore these were signi cant factors. Central Composite Design was designed using selected factors to determine and optimize their co-relation for higher productions.Asif et al. 2012 designed experiments for proteases using the similar approaches. where y is the PDC activity and PAC produced (g/L) positive sign in front of the terms indicates synergetic effect, whereas negative sign indicates antagonistic effect.

3.5.Analysis of variance (ANOVA)
ANOVA for Pyruvate decarboxylase activity showed that the F-values of Model (3.71,18.4 and 6.08), P-Values less than 0.005 forPDC activity, Sugar consumed and PAC produced implies that the models for all these responces were signi cant. Wherase P-Value for lack of t was greater than 0.005 so the lack of t was non-signi cant for all responces, which made them more signi cant ( Optimizations (Figs. 3A,3B) goals for sugar consumed, PDC activity and PAC were maximized. All possiblities of interaction and their co-relations were studied. Importance for all was of high importance i.e.,+++. 3D graphs to study the co-relation of Time, sugar concentration and temperature were plotted.
Effects of these interactions over PDC activity and PAC produced (g/L)were studied. As shown in Fig. 4A and 5A.
Linear plots (Fig.3A.) between standardized effects and Normal %age probability for PDC activity, PACand sugar Consumed during fermentation showed that the resultant values were uniformly distributed along a linear trend line. Box-Cox plots (Fig.3B.) for PDC activity, PAC and sugar Conc. showed that Lameda for PDC activity and PAC produced were near to the ideal values that is 1 for PAC and 0.048 for PDC, whereas for sugar consumed it was beyond the limit for good models. Shown by blue lines in Fig  3B. According to ANOVA and statistical analysis temperature is signi cant. As per analysis of Ramps (3C. Interactions of Temperature with sugar Conc. and Time, enhanced PAC produced but interactions of Time and Sugar conc. could not produce good yields of PAC (g/L). Standard error of interactions was uniform for all interactions (Fig.5A, 5B). Hence, through CCD design it was found that Temperature was the factor which can produce higher activity of PDC and its Products.Arroyo-López et al2009, reported temperature and sugar as signi cant parameters, affecting the microbial growth and product formation using CCD of RSM. They maximize the yield by process optimization for ve factors (initial pH, initial molasses concentration %,v/v, incubation temperature °C, mixing rate rpm, and incubation period (Hrs). In present study eleven factors through PBM and three factors through CCD were optimized to enhance the yield of PDC and PAC. Design Expert 6.0.7 software (Stat-Ease Inc., Minneapolis, USA) was reported for optimization of alcoholic fermentation. We used Design-Expert version 10.1.6 (Stat-Ease, Inc., Minneapolis, MN, USA) for optimization.According to the RSM optimization process, the response for each fermentation parameter was de ned within the studied levels range to get the highest performance.
RSM combines the individual desirability, intoa single number and searched to optimize the function, based on the response. Arroyo-López et al. (2009) calculated bioethanol production 30.7 g/L with bioethanol yield of 42% under fermentation conditions; pH 5, 25% initial molasses concentration, 35°C, %bioethanol yield. In present model maximum PDC activity was 56.27 U/ml producing 8.44 g/L PAC. The yield was increased by 71% under optimized fermentation conditions, initial pH 5.0, total sugar concentration 18% (v /v), Incubation temperature 33°C and 13hrs of incubation. Retention times (Table  3A) for PAC, Benzoic acid and Benzyl alcohol were5.5-6.0, 17.5 and 1.5-2.0 min through HPLC puri cation.
Whole cells of Pichia cecembences (Table 3B)  Effect of every variable was tested by the following equation:-E (Xi) = 2 (Σ Mi --Mi + )/ N In this equation, E (X i ) = the effect of the tested variables Miand Mi + = the total production from the trials where the variable Xi measured at low and high levels, respectively and N= the number of experiments. Responses were studied as PDC activity, Sugar consumed and nal pH. Theoretical yields were compared with actual yield to select signi cant factors.

Experimental design and process optimization by Response Surface Methodology
Signi cant factors selected by PBM were optimized by Response Surface Methodology (RSM) using central composite design (CCD).

Statistical analysis
Coded equation for signi cant factors was:- Where Z =coded value of independent variable, X = the corresponding real value; X o = real value of an independent variable at the center point and ΔX = step change of real value at the variable for Z the value.
The relationship between the response and the independent variables was explained by using second order polynomial equation:- Where, Y= predicted response,β o = the interception coe cient, β i = Linear coe cient, β ii = quadratic coe cient and β ij = interception coe cients. Software package Design-Expert version 10.1.6 (Stat-Ease, Inc., Minneapolis, MN, USA) was used for multiple regression analysis and construction of response surface models and their studies.The signi cance of regression equation was studied by F-test and lackof-t, and explained by coe cient of determination R 2 that is adjusted R 2 , predicted R 2 and coe cient of variance. The 2 nd order tted polynomial equation was explained through three dimensional graphs to show the relationship among the response and experimental factors. The maximum response of each variable was optimized through point optimization method. This method was validated through optimized variables producing maximum response.

Fermentation Technique For Pdc Production
Submerge fermentation (SmF) in shake asks was carried out for the biosynthesis of PDC. Molasses, pure cheese whey (whey without suspended proteins), whole cheese whey (whey with suspended proteins), carbon sulphite liqour, glucose, galactose, maltose and fructose were screened as carbon sources. Seed inoculum was prepared in a medium containing (g/L): treated molasses 80, Urea 1.0, MgSO 4 1.0 (pH was adjusted at 6.0) nal volume was made upto 1L with distilled water. precipitated proteins were removed by centrifugation at 6000 RPM for 15min. PAC was quanti ed through spectrophotometric analysis at 570nm. 500µl Carboligase assay mixture was mixed with colorless tetrazolium 500µl (0.1%, w/v) in presence of 1ml 3M NaOH. PAC and other products of PDC reduced colorless tetrazolium salt into red salt. Absorbance at 570 was carried out to quantify products.
One unit of Carboligase activity was de ned as the amount of PDC required to produce 1.0 mM PAC from pyruvate and benzaldehyde per min at pH 6.0 and 30°C. Calibration curves of Pyruvate, benzaldehyde, PAC, Benzoic acid and benzyl alcohol were used.

Partial puri cation of PDC
A scaled up batch for PDC production using the optimized constituents through RSM was carried out in 5L ask containing 1500ml fermentation medium. The asks were inoculated with 8% (v/v) of 24hrs old vegetative inoculum and incubated at 33 o C for 13hrs. Yeast cells were harvested after centrifugation and washed with deionized water. Enzyme extract was prepared as described earlier and was used for the partial puri cation of the enzyme through ammonium sulphate precipitation.     Figure 1 Screening of Carbon sources for Pyruvate Decarboxylase production