“Screening and Optimization of Agro-industrial wastes for glycoprotein biosurfactant production from Sphingobacterium thalpophilum DP9”

Commercialization of biosurfactant production is a big challenge due to high production cost. Biosurfactant production can be made economic by using low cost agro-industrial wastes or byproducts as media supplement. It also solves the problem of environmental pollution through waste management. In the present study attempt was made to produce biosurfactant from Sphingobacterium thalpophilum DP9 using various agro-industrial wastes or byproducts at optimum fermentation conditions evaluated by traditional and statistical methods. Partial characterization of biosurfactant was also carried out through qualitative chromatographic techniques; quantitative spectroscopic method and functional group identication by Fourier transform infrared spectroscopy. Potato peel powder, urea and temperature have signicant inuence on biosurfactant production from Sphingobacterium thalpophilum DP9. When potato peel powder and urea supplied at optimum level, biosurfactant production was increased by two fold. Biosurfactant produced by S. thalpophilum DP9 was belongs to glycoprotein class and as per our best knowledge, this is the rst report on glycoprotein biosurfactant produced from Sphingobacterium genus.


Background
Biosurfactants are biologically produced surface active amphiphilic molecules which nd their applications in various industries viz. medical (Henkel et al. 2012;Radzuan et al. 2017), pharmaceutical (Bhardwaj et al. 2013;Hu et al. 2015), petroleum (Henkel et al. 2012;Singh et al. 2019), food (Armendáriz et al. 2019;Moshtagh et al. 2019), cosmetic (Vera et al. 2018;Joy et al. 2020) and personal health care industries. They are superior to chemical surfactants because of their biodegradable (Makkar and Cameotra 1997a), biocompatible (Rodrigues and Dourado 2014), non/less toxic (Rodrigues and Dourado 2014), action speci c (Rosenberg et al. 1979) and stable nature (Kumaran et al. 2015). Major challenge about the utilization of biosurfactant is the production cost at industrial level and demand of purity in some sectors. For instance, the biosurfactant used for microbial oil enhanced recovery requires large amount of biosurfactant with less purity, however, the biosurfactant employed for cosmetics and personal health care products needs high purity as they might elicits immunological reactions (Mukherjee et al. 2006;Helmy and Kardena 2011;Soares da Silva et al. 2017).
Major challenges for industrial production are high capital cost, less e cient biosurfactant producing strains, limited knowledge of genes and pathways for biosurfactant biosynthesis which may lead to restricted gene manipulation in existing strains, less technological advances and less e cient downstream processing for biosurfactant puri cation ( One of the possible solution for industrialization of biosurfactant production, is the use of agricultural waste and/or by products as cheaper alternative production media supplements which lower the capital cost and make the process economic (Nitschke et al. 2004;Rivera et al. 2019;Nogueira et al. 2020). Moreover, it also serves the purpose of waste disposal to many agro, brewing and milk or milk product based industries in addition to environmental concern (Mercade and Manresa 1994;Banat et al. 2014).
The choice of right agricultural waste and/or byproduct with correct balance of carbohydrate, lipid, protein and other mineral salts is important not only to support organisms growth but also helps in production of secondary metabolites like biosurfactants (Helmy and Kardena 2011). On the basis of such background knowledge the present study was conducted to screen various agricultural wastes or byproducts for the biosurfactant production at optimum fermentation conditions as per the traditional as well as statistical method. These are the conventional but effective strategies for improvement of production (Singh et al. 2019).

Microorganisms and its maintenance
In the present study, Sphingobacterium thalpophilum DP9 -Gram negative bacteria (GenBank accession number: MG-000135) isolated in our laboratory from automobile workshop soil (Anand, Gujarat, India N 22°54' and E 72°95') was used. The bacterial culture was maintain in Luria Bertani (LB) agar slants at 4 °C and was sub-cultured at every three month after con rming purity of culture by Gram's staining.
Inoculum preparation, Medium composition and culture conditions Inoculum was prepared by adding a well isolated colony from L.B agar plate in to 100 ml of LB medium maintained at 30 °C with 150 rpm shaking condition overnight. Next day, required amount of inoculum (after O.D. 600 reaches to 1.00) was added to production medium.
Bushnell-Haas medium (BHM) added with carbon and nitrogen source was used as production medium. Media were sterilized by autoclaving 121 °C for 15 min. The screening of factors viz. carbon source, nitrogen source, temperature, pH, inoculum size and aeration were carried out primarily by One-Factor-At-Time (OFAT) method. Afterwards, Placket-Burman design made by Microsoft O ce-2007 with Excel add-on , was employed to screen signi cant factor.
Carbon source used were rice straw, corn straw, wheat straw, maize bran, wheat bran, potato peel powder, cotton seed cake, sugarcane husk, ground nut husk, coconut oil cake, and sugarcane molasses.
Nitrogen sources used were urea, peptone, ammonium sulfate, ammonium nitrate, ammonium chloride, sodium nitrate, yeast extract and malt extract.

Screening of agricultural wastes or byproducts by One-Factor-at A -Time [OFAT] method
OFAT method was used to screen optimum carbon source (at 1% concentration), nitrogen source (0.1% concentration), inoculum size (0.5 % to 4 % when 1.00 O.D. 600 ), pH (5 to 11), temperature (25 °C to 45 °C) and aeration ( by varying the headspace volume in 250 ml conical ask by changing the medium volume in the ask by 10 % to 50 % v/v of ask i.e. 250 ml as suggested by Abdel-mawgoud et al. 2008)). Carbon sources and nitrogen sources were screened by separately added in 100 ml of production medium (i.e. BHM). Screening of aeration was carried out by preparing production media with different volume viz. 25 ml, 50 ml, 75 ml, 100 ml, 125 ml and 150 ml in 250 ml Erlenmeyer ask. Later on, pH, inoculum size and temperature were screened sequentially. All media were kept under 150 rpm shaking condition and response was checked for 10 days by emulsi cation index (E24% test) as suggested by Shahaliyan et al. (2015).

Plackett-Burman Design
Plackett-Burman design was used to nd out important medium component (carbon or nitrogen source) and production parameter (temperature and pH). Plackett-Burman design matrix also assumes that there is no interaction between variables considered in present study in the range. It consents to explore up to 'N-1' variables with N experiments without inter-component interactions. In present study, four variables were selected viz. potential carbon source, potential nitrogen source, pH and temperature. Each variable was checked at two level (-1, +1 or low level and high level) as given in Table-1. Plackett-Burman design matrix is a full factorial design and main effects of design is simply calculated as per the difference between average value made at the high level values (+) of the factor and average of measurement at the low level values (-). The critical factors are identi ed through this experimental matrix and then Central Composite Design (CCD) was used to obtain quadratic model. Where Y is the response in terms of protein assayed as per the method suggested by Lowery et al. (1951), βi are regression coe cients, is the level of the independent variable. The experiments were conducted three times and results were noted in mean ± standard deviation (Mnif et al. 2012;Anvari et al. 2015;Ekpenyong et al. 2017).

Central Composite Design (CCD)
Following the screening of signi cant media components by Plackett-Burman design, relationship among the quantitative factors and the response (biosurfactant production) was carried out by central composite design (CCD) under response surface methodology (RSM). Best response was evaluated by permutation of the factor levels (El-Gherab et al. 2019). Signi cant factor obtained after analysis by Plackett-Burman design, was used to create CCD matrix and statistical analysis. Most optimum level, impact and interaction of factors i.e. carbon source (X 1 ), nitrogen source (X 2 ) and temperature (X 3 ) at three coded level (-1, 0 and +1) as shown in the table-3 by full factorial (2 3 ) CCD using Design s (X 2 ) and pH (X 3 ) were optimized by full factorial ( Where, Y-Predicted response (Emulsi cation index; indirectly biosurfactant production); β 0 -Intercept; X 1 -carbon source concentration (g/l); X 2 -Nitrogen source (%w/v); X 3 -Temperature; β 1 , β 2 and β 3 -linear co-e cient; β 12 , β 23 and β 13interaction coe cient; X 1 2 , X 2 2 , X 3 2 , X 1 X 2 , X 2 X 3 and X 1 X 3 -interaction between the variables as signi cant terms.

Experimental Validation of statistical model
From the response surface design the optimum experimental conditions were tested and validated three times. Results were recorded as in terms of mean ± standard deviation.

Extraction and puri cation of biosurfactants
Cell free supernatant was collected from each ask prepared as per the optimized and validated process for biosurfactant production by centrifugation (6000 rpm for 20 min) and pH was set 2.00 using 6.0 N HCl. Broth was preserved at 4 º C till visible precipitates observed. Precipitates were collected by centrifugation and kept for air drying. Biosurfactant was collected as dry powder by scraping.
Carbohydrate was detected by treating the TLC with α-naphthol followed by sulfuric acid. Protein was detected by spraying 0.3% ninhydrine solution and for lipid content iodine vapor was used.

Spectrophotometric qualitative test
Biosurfactant powder was dissolve in distilled water to get 10 mg/ml of concentration. Carbohydrate, protein and lipid contents were estimated by phenol-sulfuric acid test, Folin-Lawry method and GPO/POD method (according to diagnostic triglyceride kit procured from Sigma Dignostic Pvt. Ltd. Vadodara, India), respectively.

Electrophoretic studies
Biosurfactant was further analyzed through sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by coomassie brilliant blue stain, silver stain. Native-PAGE followed by Periodic acid-Schiff (PAS) stain as per the standard protocol.

Results And Discussion
The perspective of maximizing biosurfactant yield can be ful ll by utilizing agricultural waste or byproducts which not only help to cut down the capital cost but also helps in waste management. In present study, we have screen eleven agricultural wastes or byproducts for their e cacy as media supplement. The right choice of agricultural waste is very important because the biosurfactant yield, its type and purity is solely depends on the media constituents (Helmy and Kardena, 2011). Agricultural waste or byproducts provides adequate balance of carbohydrate and lipid to the bacteria along with other nutrients like protein, mineral salts etc. for their growth and also for production of secondary metabolites like biosurfactant (Mercade and Manresa, 1994). Therefore, by using a single agricultural waste or byproduct, the production of biosurfactant can be made economic. In present study, we have screened eight different nitrogen sources for their impact on biosurfactant. They are either organic compound or inorganic salt.
In order to maximize production two methods were adopted. First OFAT to screen effective carbon source, nitrogen source, pH, temperature, % inoculum and aeration, which is followed by statistical approach i.e. Plackett-Burman Design using Microsoft

OFAT analysis
One factor at a time method for optimization is traditional technique used to screen effective agricultural waste or byproduct.
The major advantage of this method is its simplicity (because it does not require any statistical analysis) and results can be narrated by simple graphs (Hema et al. 2019). In present study, among the all agricultural wastes or byproducts listed above highest emulsi cation index was observed in potato peel powder containing medium (E24% = 43.12 ± 3.41%), which was followed by deoiled coconut oil cake (E24% = 31.00 ± 8.27%) and wheat bran (E24% = 31.47 ± 3.41%) ( Fig.1(a)). Bacillus subtilis DDU20161 was reported to produce 253.79 gm/L of biosurfactant when cultured in media containing potato peel and pulp during 40 h of incubation time under stirred tank bioreactor (Pande et al. 2020); while potato peel signi cantly effect on emulsi cation of biosurfactant from Bacillus subtilis SNW3 (Naeem 2018). The emulsi cation index of biosurfactant produced by B. subtilis SNW3 strain was 15. 48% when bacteria were grown in media supplemented with potato peel (Naeem 2018). In case of B. licheniformis J1 potato peel was act as biostimulator to increase the petroleum oil degradation potential (Jyoti and Rajesh 2018). According to Javed et al. (2019) potato peel is an economy booster for developing countries because it is inexpensive waste of food processing industry and can support many of the industries viz. backing industry, biogas production industry, lactic acid production industry, enzyme production industry, bio-fertilizer industry, bio-fuels industry, bio-sorbent industry, pharmaceutical industry and biosurfactant production industry.
Whereas, urea was less preferred by B. subtilis RSL2 for the production of biosurfactant (Sharma and Pandey 2020) while, for Arthrobacter para nes ATCC 19558 urea was preferable organic source for biosurfactant production (Ezebuiro et al. 2019).
Therefore, potato peel powder and urea were used as carbon and nitrogen sources respectively, under 40% of aeration for further studies. Bacterial inoculum used was 1% with O.D. 600 =1.00, pH 7 and incubation temperature was kept 30 °C.

Optimization by Plackett-Burman Design
Once the optimum media components and production parameter was screened by traditional OFAT method, Plackett-Burma design was used for nd out most in uencing factor on biosurfactant production among the carbon source, nitrogen source, temperature and pH. The design generated by Microsoft O ce-2007 with Excel add-on given in Table-2. The Plackett-Burman design for eleven experiments with four variables (i.e. carbon source, nitrogen souse, temperature and pH) shown in Table-2. The protein concentration (indirectly biosurfactant production) was considered as response. The results achieved from the experimental run was noted as in Table-2 and it showed variation from 38.4 μg/ml (run no: 12) to 265.9 μg/ml (run no: 5). Data was subjected to statistical analysis using Microsoft O ce-7 Excel add-on, to calculate main effect, standard error F-value, pvalue and 90% clearance. Analysis of the data showed in Table-3. Components showed more than 90% clearance were considered as most in uencing factor on biosurfactant production.
From the obtained data it was clear that carbon source, temperature and nitrogen source showed clearance value above 90% than i.e. 97.93%, 92.35% and 92.25%, respectively. Therefore, most in uencing factor was carbon source (in present study potato peel powder) followed by temperature and nitrogen source (in present study urea).
After nding the critical factors i.e. potato peel powder, temperature and nitrogen source, response surface methodology was used to study interactions between factors and to nd out exact concentration or values of the variables for biosurfactant production through CCD.
Optimization of media component and production parameter by Central composite Design (CCD) Selected variables which gave signi cantly in uence on biosurfactant production were optimized. Experiment was carried out using CCD which consisted of three levels i.e. low level, high level and central point for each variable. The design and obtained values are described in Table-4. Highest response (E24% = 50%) was obtained in run-4 (5 gm% potato peel powder, urea 1.5 gm% and temperature 45 °C) and lowest response (E24% = 52%). ANOVA analysis was carried out to scrutinize the variability produced by a factor. Regression analysis showed urea (X 2 ) and temperature (X 4 ) were signi cant while potato peel powder (X 1 ) was insigni cant (p < 0.05) variables. Linear positive co-e cient values of all three variables were non-signi cant. The interaction between X 2 -X 4 (urea and temperature; p=0.0248) was signi cant while X 1 -X 2 (potato peel powder and urea) as well as X 1 -X 4 (potato peel powder and temperature) were insigni cant. The correlation co-e cient R 2 indicates accuracy of model and it was 76.94% and adjusted R 2 value was 56.19%. Second-order polynomial equation was used to study effect of factors on E24% (indirectly biosurfactant production). The equation for present study was as follow: [Please see the supplementary les section to view the equation.] Here, Y is response i.e. E24% (indirectly biosurfactant production); X 1 carbon source (potato peel powder), X 2 nitrogen source (urea) and X 4 temperature.
To evaluate the optimum value of each factor for highest E24% (or biosurfactant production), 3D response surface plots ( Figure-3) were created in Design Expert software by plotting the response function of two factors while keeping another at central point.
The signi cant interaction between X 2 and X 4 was obtained from response surface plot.
It was clear from the 3D plots that, interaction between X 1 -X 2 and X 1 -X 4 the center point of carbon source is 3.2 gm%.
Beyond this concentration the predicted value of E24% was increased but, technically, the values of E24% never go beyond 100%. Therefore, values of variables were selected in such a way that response resided below 100%. Similarly, as the values of nitrogen source goes near to 4 mg% and temperature near to 28 °C, the response or value or E24% was predicted approximately to be 100%. Hence, for validation purpose, the E24% (response) was predicted 100% under optimized condition and veri ed by experimentation carried out in triplicates. Under un-optimized conditions, the E24% was 43.62 ± 2.55%; which was increased after optimization to 86.11 ± 3.47%. The achieved results proposed considerable accuracy in developed model and model validation under the prescribed conditions. The data are comparable with the results reported in case of B. aryabhattai strain ZDY2 by (Yaraguppi et al. 2020). Author documented that, B. aryabhattai ZDY2 produced 2.51 fold higher biosurfactant under optimized media when supplied with 4.0% cruide oil, 0.7% yeast extract and 3.0 % NaNO 3 . Bacillus subtilis SPB1 produced 1.65 fold higher biosurfactant with optimized media supplemented with glucose, urea and K 2 HPO 4 at 15 g/L, 6 g/L and 1g/L of concentration respectively (Mnif et al. 2012).

Partial characterization of biosurfactant
Biosurfactant produced by S. thalpophilum DP9 from optimized media was extracted simply by acid precipitation. Partial puri ed biosurfactant was analyzed for presence of carbohydrate, protein and lipid qualitatively by TLC and quantitatively by spectrophotometric methods. Thin layer chromatography results indicated presence of carbohydrate (Rf = 0.71) and protein (Rf = 0.31). Lipid was absent in the biosurfactant. Spectroscopic qualitative analysis revealed that biosurfactant contain 439.58 ± 0.0129 μg/ml of carbohydrate and 507.4 ± 0.0064 μg/ml of protein, while lipid was absent. On the basis of qualitative and quantitative tests, it was assumed that the biosurfactant was glycoprotein in nature.
Glycoprotein nature of biosurfactant was con rmed by PAS staining Native-PAGE gel. Moreover, the size of the protein part present in biosurfactant was evaluated by SDS-PAGE electrophoresis followed by Coomassie Brilliant Blue (CBB) staining and Silver staining. Approximate size of protein part carried by biosurfactant was found between 42 kDa to 51 kDa. We assumed that it was near to 48 kDa of size. The presence of single band in all three staining i.e. PAS staining, CBB staining and silver staining at almost same place indicated that the biosurfactant obtained was relatively pure. Further purity can be con rmed by High Pressure Liquid Chromatography (HPLC). Results are contradictory to previous reports. Burgos-Díaz et al. (2011) have reported that the Sphingobacterium stain 6.2S produces mixture of biosurfactant which belongs to lipopeptide, phospholipud or glycolipid. Similarly, Noparat et al. (2014) have documented that S. spiritivorum AS43 produced lipopeptide biosurfactant which having 50.2% lipid and 38.5% or protein in its structure. To our best knowledge, this is the rst report of glycoprotein biosurfactant production from Sphingobacterium genus.

FTIR analysis
Molecular composition of partially puri ed biosurfactant was carried out by Fourier transform infrared spectroscopy. Weak stretching peak at 3292.61 cm -1 indicated presence of stretching vibrations from -NH of peptide (Noparat et al. 2014). Strong peaks at 3009.51 cm -1 and 2927.05 cm -1 indicated -CH stretching vibrations from -CH 2 and -CH 3 group of aliphatic chain (Burgos-Díaz et al. 2011;Noparat et al. 2014;Yaraguppi et al. 2020  phenol or -C-O of ester, ether, alcohol or carboxylic acids. 77=22.32 cm -1 peak occurred due to vibrations of primary or secondary amines or due to methylene scissoring vibrations of protein moiety (Yaraguppi et al. 2020). Hence, from the FTIR chromatogram (peaks at 1652.17 cm -1 , 1541.01 cm -1 , 1463.71 cm -1 and 722.32 cm -1 ) the presence of peptide bond, peptide moiety, carbohydrate protons and primary as well as secondary amines were con rmed which again indicated that the biosurfactant is belongs to glycoprotein class.

Conclusions
To make the biosurfactant production economic, agro-industrial waste or byproducts can be use as media supplement. In present study, eleven carbon agro-industrial waste or byproducts used as carbon sources and eight (organic as well as inorganic) nitrogen sources were screen for better biosurfactant production by conventional one factor at a time method. Other parameters screened by OFAT were pH, temperature, inoculum size and aeration. OFAT experimentations showed potato peel powder and urea were good carbon and nitrogen sources for biosurfactant production when it supplied in media with 40% of aeration and pH 7. Inoculum size for optimum biosurfactant production was 1% (v/v OD 600 = 1.00) and temperature was 30 °C.
To nd out most signi cant factor in uencing on biosurfactant production Plackett-Burman matrix design was used. Results showed, out of four variables (potato peel powder, urea, temperature and pH) considered carbon source, nitrogen source and temperature were most in uencing factors on biosurfactant production. Later on interactions studies was carried out to nd optimal level of variables for maximum biosurfactant production. Study showed nitrogen and temperature were signi cantly effect on biosurfactant production. Experimental validation based on second order polynomial equation for quadratic model showed, biosurfactant production increased to 86.11 ± 3.47% (emulsi cation) from 43.62 ± 2.55% (emulsi cation) when supplied with 3.2 gm% potato peel powder as carbon source and 4.0 gm% urea as nitrogen source and incubated at 28 °C for four days. Qualitative analysis by thin layer chromatography showed presence of carbohydrate and protein moiety in biosurfactant, which was con rmed by quantitative analysis which showed presence of 439.58 ± 0.0129 µg/ml carbohydrate and 507.41 ± 0.0064 µg/ml protein part. Lipid was absence in it. Native-PAGE staining followed by PAS staining con rms that the biosurfactant was glycoprotein in nature. SDS-PAGE study reveals that protein moiety have approximately 48 kDa of size.
FTIR chromatogram picks occurred at 1652.17 cm − 1 , 1541.01 cm − 1 , 1463.71 cm − 1 and 722.32 cm − 1 which indicated presence of peptide bond, peptide moiety, carbohydrate protons and primary as well as secondary amines which also con rms glycoprotein class of biosurfactant from S. thalpophilum DP9. As far as our best knowledge this is the rst report for production of glycoprotein biosurfactant from Sphingobacterium genus.    Optimization of (a) aeration, (b) pH, (c) temperature and inoculum size for biosufactant production  FTIR chromatogram of biosurfactant produced from Sphingobacterium thalpophilum DP9

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