Scholarly article on topic 'Statistical optimization of antifungal iturin A production from Bacillus amyloliquefaciens RHNK22 using agro-industrial wastes'

Statistical optimization of antifungal iturin A production from Bacillus amyloliquefaciens RHNK22 using agro-industrial wastes Academic research paper on "Chemical sciences"

CC BY-NC-ND
0
0
Share paper
Academic journal
Saudi Journal of Biological Sciences
OECD Field of science
Keywords
{" Bacillus " / Biosurfactants / Phytopathogens / "Antifungal activity" / "Iturin A"}

Abstract of research paper on Chemical sciences, author of scientific article — P. Narendra Kumar, T.H. Swapna, Mohamed Yahya Khan, Gopal Reddy, Bee Hameeda

Abstract Biosurfactants are secondary metabolites with surface active properties and have wide application in agriculture, industrial and therapeutic products. The present study was aimed to screen bacteria for the production of biosurfactant, its characterization and development of a cost effective media formulation for iturin A production. A total of 100 bacterial isolates were isolated from different rhizosphere soil samples by enrichment culture method and screened for biosurfactant activity. Twenty isolates were selected for further studies based on their biosurfactant activity [emulsification index (EI%), emulsification assay (EA), surface tension (ST) reduction] and antagonistic activity. Among them one potential isolate Bacillus sp. RHNK22 showed good EI% and EA with different hydrocarbons tested in this study. Using biochemical methods and 16S rRNA gene sequence, it was identified as Bacillus amyloliquefaciens. Presence of iturin A in RHNK22 was identified by gene specific primers and confirmed as iturin A by FTIR and HPLC. B. amyloliquefaciens RHNK22 exhibited good surface active properties and antifungal activity against Sclerotium rolfsii and Macrophomina phaseolina. For cost-effective production of iturin A, 16 different agro-industrial wastes were screened as substrates, and Sunflower oil cake (SOC) was favouring high iturin A production. Further, using response surface methodology (RSM) model, there was a 3-fold increase in iturin A production (using SOC 4%, inoculum size 1%, at pH 6.0 and 37°C temperature in 48h). This is the first report on using SOC as a substrate for iturin A production.

Academic research paper on topic "Statistical optimization of antifungal iturin A production from Bacillus amyloliquefaciens RHNK22 using agro-industrial wastes"

Accepted Manuscript

Statistical optimization of antifungal iturin A production from Bacillus amylo-liquefaciens RHNK22 using agro-industrial wastes

Saudi Journal of Biological Sciences

PII: DOI:

Reference:

P. Narendra Kumar, T.H. Swapna, Mohamed Yahya Khan, Gopal Reddy, Bee Hameeda

S1319-562X(15)00209-0 http://dx.doi.org/10.1016/j.sjbs.2015.09.014 SJBS 550

To appear in:

Saudi Journal of Biological Sciences

Received Date: 27 April 2015

Revised Date: 1 September 2015

Accepted Date: 6 September 2015

Please cite this article as: P. Narendra Kumar, T.H. Swapna, M.Y. Khan, G. Reddy, B. Hameeda, Statistical optimization of antifungal iturin A production from Bacillus amyloliquefaciens RHNK22 using agro-industrial wastes, Saudi Journal of Biological Sciences (2015), doi: http://dx.doi.org/10.1016/j.sjbs.2015.09.014

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Statistical optimization of antifungal iturin A production from Bacillus amyloliquefaciens RHNK22 using agro-industrial wastes

P. Narendra Kumar, T.H. Swapna, Mohamed Yahya Khan, Gopal Reddy, Bee Hameeda*

Dept of Microbiology, University College of Science, Osmania University, Hyderabad 500 007.

Bee, Asst. Pn yderabac

Correspondence: Dr. Hameeda Bee, Asst. Professor, Dept of Microbiology, University College of

Science, Osmania University, Hyderabad 500 007.

Corresponding author * Email id: drhami2009@gmail.com Phone No: 040-20052737, Fax 040-27090661

Statistical optimization of antifungal iturin A production from Bacillus amyloliquefaciens RHNK22 using agro-industrial wastes

Abstract

Biosurfactants are secondary metabolites with surface active properties and have widi application in agriculture, industrial and therapeutic products. The present study was aimed to screen bacteria for production of biosurfactant, it characterization and development of a cost

effective media formulation for iturin A production. A total of 100 bacterial isolates were isolated from different rhizosphere soil samples by enrichment culture method and screened for biosurfactant activity. Twenty isolates were selected for further studies based on their biosurfactant activity [emulsification index (EI %), emulsification assay (EA), surface tension (ST) reduction] and antagonistic activity. Among them one potential isolate Bacillus sp. RHNK22 showed good EI % and EA with different hydrocarbons tested in this study. Using biochemical methods and 16S rRNA gene sequence, it was identified as Bacillus amyloliquefaciens. Presence of Iturin A in RHNK22 was identified by gene specific primers and confirmed as iturin A by FTIR and HPLC. B. amyloliquefaciens RHNK22 exhibited good surface active properties and antifungal activity against Sclerotium rolfsii and Macrophomina phaseolina. For cost-effective production of iturin A, 16 different agro-industrial wastes were screened as substrates, and sunflower oil cake (SOC) was favouring high iturin A production. Further, using response surface methodology (RSM) model, there was 3-fold increase of Iturin A production (using SOC 4 %, inoculum size 1 %, at pH 6.0 and 37 °C temperature in 48 h). This is first report on using SOC as substrate for Iturin A production.

Keywords: Bacillus, Biosurfactants, Phytopathogens, Antifungal activity, Iturin A

cords: Baci

1. Introduction

Microbial surfactants are structurally diverse group of surface active molecules produced by a wide variety of microorganisms, including bacteria, fungi and yeasts. These are amphiphilic molecules with both hydrophilic and hydrophobic moieties that confer the ability to accumulate between fluid phases, thus reducing surface tension at surface and interface respectively (Ongena and Jacques, 2008; Mukherjee et al., 2006). Biosurfactants have advantages over their synthetic counter parts due to their low toxicity, higher biodegradation, better environmental compatibility at extreme temperature, pH, salinity and their ability to be synthesized from renewable feedstock ((Romero et al., 2007). Biosurfactants have potential to be applied in pharmaceutical, cosmetics, petroleum, food industries and agriculture sector.

Global demand for microbial biosurfactant is valued at USD 12.7 million in 2012 and

is expected to reach USD 17.1 million by 2020, expanding at a Compound annual growth rate

(CAGR) of 4 % from 2014 to 2020. Of the different biosurfactants, lipopeptides have projected peak annual US revenue of >US $1 billion and are approved in more than 70 countries (Meena and Kanwar, 2015; www.transparencymarketresearch.com). Members of the Bacillus genus are considered as efficient microbial factories for large scale production of lipopeptides such as iturin, surfactin and fengycin, inhibiting various fungal pathogens and protecting the crop plants (Singh et al., 2014; Jin et al., 2014). However, significant obstacle to meet the large scale industrial application of biosurfactants is the high production cost (Makkar and Cameotra, 2002). Hence, optimization of medium composition is most important for the production of microbial metabolites at industrial scale because around 30-of production cost is estimated to be the cost of growth medium (Dhanya et al., 2008; a et al., 2014). Agro-industrial wastes contain high amount of carbohydrate, proteins, ipids and are generally used as cattle feed or composted and disposed into land fill. Instead they can be used as substrates for cost effective production of microbial metabolites such as biosurfactants (Yarchoan and Arnold, 2014).

40 % o Radhik lipi the

In microbial fermentation, potentially influential variables are numerous and when desired to screen a large number of factors; experimental designs for first-order models, such as the factorial design or Plackett-Burman design, can be used. Plackett-Burman factorial

designs are used for reliable short listing of medium components in fermentation for further optimization and allow one to obtain unbiased estimates of linear effects of all factors with maximum accuracy for a given number of observations. However, they do not give an optimum value for each variable and further optimization is needed (Ikram and Ali, 2005). Response surface methodology (RSM) has been widely used to evaluate and understand the interactions between different physiological and nutritional parameters (Laxman et al., 2005). RSM, which includes factorial design and regression analysis and can be used to evaluate the effective factor, build models, provide information about the interaction between variables and multiple responses at the same time (Dhouha et al., 2012). The objective of this work are to screen rhizosphere bacteria for biosurfactant production, characterize high biosurfactant producing bacterial isolate and develop a cost effective medium formulation for iturin A production.

2. Material and methods

2.1. Media used for growth of microorganism,

m formul /

isms .....

Nutrient broth (NB) and mineral salts medium (MSM) [NaNO3 (0.5), MgSO4.7H2O (0.5), KCl (0.1), FeSO4 (0.01) K2HPO4 (0.5), KH2PO4 (0.5) g/L], pH 7 and temperature 37 0C were used for bacterial growth. Potato dextrose agar (PDA), glucose casaminoacids yeast extract medium (GCY) and Kings B (KB) medium were used for fungal growth at 30 0C.

2.2 Isolation of biosurfactantproducing bacteria by enrichment culturing

Rhizosphere soil samples of varied crop plants were collected from different areas of Telangana and Andhra Pradesh states of India. Enrichment culturing method was performed by the method described by Dubey and Juwarkar (2001), 10 g of soil sample was added to L of mineral salts medium (MSM) in 250 mL flasks, amended with kerosene (1 mL or 5 mL or 10 mL) for enrichment and incubated at 37 °C, 180 rpm for 72 h. The enriched soil samples were subjected to serial dilution and appropriate dilutions were spread on nutrient agar plates and incubated at 37 °C for 24-48 h. Colonies of pure cultures were isolated and further characterised by Gram's staining and spore staining. The cultures of selected Bacillus spp., were persevered as glycerol stocks (-70 °C) for further studies.

2.3. Preliminary screening for biosurfactant activity

From the above preliminary screening, hundred bacterial isolates were isolated and tested for biosurfactant activity using following methods like microplate and penetration methods, oil-spread method, blue-agar plate method, blood haemolysis test, lipase assay, emulsification index (EI) and emulsification assays (EA) for preliminary qualitative screening of biosurfactant activity. All the bacterial isolates were inoculated in NB medium and incubated at 37 °C for 24 hours. Based on the assay, overnight grown culture or cell free supernatant

was used as required.

2.3.1. Microplate method

In this method the culture supernatant (100 ^L) of each bacterial isolate was added separately into 96 well microplate placed on graph paper. Then the plate was observed for curvature of lines on graph sheet under each well. Curvature of graph lines under the well is a preliminary indication of biosurfactant activity (Vaux and Cottingham, 2001).

2.3.2. Penetration method

All the wells of 96 well microplates were filled with 150 ^L hydrophobic paste consisting of oil and silica gel. Then the paste was overlaid with 10 ^L of oil. To this, 90 ^L of cell free supernatant and 10 ^L of staining solution (safranin) were added and biosurfactant activity was identified. Based on the results obtained by microplate and penetration methods, twenty isolates were selected for further studies.

2.3.3. O, Oil

il spread method

spread assay described by Plaza et al. (2006) was performed according to which 50 mL of distilled water was taken in a Petridish, and 20 ^L of crude oil was overlaid uniformly on water surface. Then, 10 ^L of cell free supernatant was added over the oil surface and biosurfactant activity was measured by observation of clear zone on oil/oil displacement.

2.3.4. Blue agar plate

MSM supplemented with glucose (2 %) and cetyl trimethyl ammonium bromide (CTAB: 0.5 mg/mL) and methylene blue (0.2 mg/mL) was prepared. 30 ^L of 24 h old culture of each isolate was spot inoculated onto the blue agar plates. Extracellular anionic surfactants form insoluble complex with CTAB and methylene blue, resulted in blue colored halo around the colony (Siegmund and Wagner, 1991).

2.3.5. Blood haemolysis test

!. 20 uL

Haemolysis activity was performed on 5 % sheep blood agar plates. 20 ^L of 24 h old culture of each isolate was spot inoculated on blood agar medium, incubated at 37 °C for 48 h and observed for clear zone of haemolysis around the bacterial colonies indicating the presence of biosurfactant activity (Anandaraj and Thivakaran, 2010).

2.3.6. Lipase assay (Tributyrin agar plate method)

Lipase assay was performed by the method described by Kokare et al. (2007), where 20 ^L of 24 h old cultures were spot-inoculated on tributyrin agar medium [containing (g/L) peptone 5, beef extract 3, tributyrin 10, and agar-agar 20], incubated at 37 °C for 48 h and observed for clear zone of lysis around the colonies.

2.3.7. Emulsification index (EI %)

Twenty bacterial isolates were grown in 100 mL of NB medium in 500 mL Erlenmeyer flask at 37 oc on rotary shaker (180 rpm) for 48 h. Supernatants were centrifuged at 10,000 rpm for 15 min at room temperature. Emulsification index (EI %) was measured by adding 2 mL of culture supernatant to 2 mL of each hydrophobic substrates [kerosene, benzene, coconut oil, toluene, diesel, engine oil (red oil), petrol, xylene and sunflower oil], mixed vigorously for 10-15 min and the mixture was left undisturbed for 24 h. EI % was calculated using the formula (1).

Emulsification index (EI %) = Height of the emulsion x 100

Total height

2.3.8. Emulsification assay

Emulsification assay was performed according to the method described by Satpute et al. (2008) with slight modifications. In this method, 3 mL of culture supernatant was mixed w 0.5 mL of different hydrocarbon oils, vortexed for 2 min and left undisturbed for 1 h at room temperature until a separate aqueous and oil phase was observed. Then the absorbance of aqueous phase was measured at 400 nm.

2.4. Surface tension measurement

Biosurfactant production was monitored by surface tension measurement using a Du Nouy ring type tensiometer (Nitschke and Pastore, 2006). Culture free supernatants of bacterial isolates grown for 24 h and 48 h were tested for reduction in surface tension. The surface tension was measured at room temperature after dipping the platinum ring in solution for a while in order to attain equilibrium condition. For calibration of the instrument, the surface tension of distilled water was first measured and later the supernatant, as prepared above, was measured and uninoculated sterile broth was used as control. Average value of triplicate was used to express the surface tension of the sample (Cooper and Zajic, 1980).

2.5. Anti-fungal acü

Anti fungal activity was detected by the dual culture method. Soil borne plant pathogenic fungi, Sclerotium rolfsii and Macrophomina phaseolina were grown on PDA, GCY and KBM media. An agar block (five mm dia) was cut from an actively growing (96 h old) fungal culture and placed on the surface of fresh agar medium at the centre of Petri plate. A loopful ' 24 h old culture of each bacterium was streaked in a straight line on one edge of a 90 mm dia Petri plate, plates were incubated at 30±2 °C and the inhibition zone between two cultures was measured 5 days after inoculation. Plates inoculated with the same fungus without bacteria were used as control. Three replications were maintained for each and reduction in radial growth was measured and percent inhibition over control was calculated using the formula (2).

Where,

I = Inhibition % of mycelial growth (growth reduction over control) C = Radial growth of fungus in the control plate (mm) T = Radial growth of fungus on the plate inoculated with bacteria (mm)

Based on the screening results for biosurfactant and antifungal activity, one potential bacterial isolate RHNK22 was selected for further studies. It was identified morphologically by Gram's staining and biochemical tests like starch hydrolysis, Indole test, Methyl red test, Voges-Proskauer test, Citrate utilization, Catalase test and Glucose fermentation test. All tests were performed according to the Bergey's Manual of Systematic Bacteriology (Sneath et al., 1986).

2.7. Molecular identification of RHNK22 based on 16S rRNA gene sequence

For molecular identification, the isolate was sent to MACROGEN (Seoul, Korea) for sequencing using universal 16S rRNA primers. Phylogenetic analysis was done by using mega-4 bioinformatics software. 16S rRNA sequence submitted to EMBL and Nucleotide Sequence Database Accession number is LM651914.

2.8. Detection of Iturin A gene in RHNK22 by PCR analysis

Bacterial isolate RHNK22 was inoculated into 5 mL of LB broth and incubated at 37 0C on rotary shaker at 180 rpm for 18-24 h. DNA extraction was done using DNA isolation kit by Xcelris labs, India. Iturin A gene specific primers F; TCC AGA CAA TGA CGG ATG GC; R; TTG AAG GAC CAC GAG TTC GG used in the present study were designed by Primer 3 software. Iturin A gene specific primers designed by Ramaratham et al. (2007) F; GATGCGATCTCCTTGGATGT; R; ATCGTCATGTGCTGCTTGAG were used as positive control. Iturin A gene sequences, (GenBank acc. No. AF534617.1) were searched in the databank through the NCBI Blast search. PCR reaction was performed according to the

standard protocols. PCR conditions are 95 °C - 5 min. 30 cycles (94 °C for 1 min, 50 °C for 1 min, 72 °C for 1 min) and 72 °C for 5 min and observed for PCR product size 647 bp.

2.9. Production and extraction of biosurfactant

Bacterial isolate RHNK22 was grown in 100 mL of MSM in 500 mL Erlenmeyer fla incubated at 37 °C, 180 rpm for 48 h and then centrifuged at 10,000 rpm for 15 min. The supernatant was collected and its pH was adjusted to 2.0 using 6N HCl and kept at 4 °C for overnight to allow for precipitation. The precipitate was centrifuged at min. The biosurfactant was dissolved in methanol and dried in rotar (Kim et al., 2004).

for 20 m evaporator

ained from RHNK22

2.10. Antifungal activity of the extracted surfactant obtain

Extracted biosurfactant was dissolved in distilled water and tested for antifungal activity. For testing antifungal activity, an agar plug of actively growing fungal culture was placed in the centre of the plate. Then, wells were made (using sterile agar borer), 2 cm away from the centre where fungus was placed and different concentrations (0.07 mg/mL, 0.15 mg/mL, 0.31 mg/mL, 0.62 mg/mL, 1.25 mg/mL, 2.5 mg/mL and 5 mg/mL) of biosurfactant were added in separate wells and incubated at 30±2 °C for 24 to 96 h. Sterile distilled water was used as control. The inhibition percentage (I %) was calculated using the following formula (3) (Anandaraj and Thivakaran, 2010; Murata et al., 2013).

rhivakar

I = Inhibition % of mycelial growth (growth reduction over control) C = Radial growth of fungus in the control plate (mm) T = Radial growth of fungus on the plate inoculated with bacteria (mm)

2.11. HPLC analysis of biosurfactant

HPLC analysis of the extracted biosurfactant was performed by LC-2000 system (Shimadzu) with a C18 column (Phenomenex luna C18). Preparation of mobile phase components were 0.1 % trifluoroacetic acid (TFA) in methanol. The products were eluted at a flow rate of 1.0 mL/min and elution pattern was monitored by determining absorbance at 210 nm. Pure iturin A (Sigma-Aldrich Co. USA) was used as standard.

2.12. FTIR (Fourier transform infrared spectroscopy) analysis

acted bi

To understand the overall chemical nature of the extracted biosurfactant, FTIR was employed. The technique helps to explore the functional groups and chemical bonds present in the crude extract. The analysis was done using FTIR spectrophotometer. Samples were prepared by homogeneous dispersal of 1 mg of biosurfactant sample in pellets of potassium bromide. Infra red (IR) absorption spectra were obtained using a built-in plotter. IR spectra were collected over the range of 450-4500 cm-1 with a resolution of 4 cm-1. The spectral data given are the average of 10 scans over the entire range covered by the instrument.

2.13. Selection of agro-industrial wastes for iturin A production by unidimensional approach

Initially 16 different agro-industrial wastes i.e., rice bran husk (RBH), Sunflower oil cake (SOC), Coconut oil cake (COC), Cotton seed oil cake (CSOC), Corn cob (CC), Orange peel (OP), Jack fruit peel (JFP), Sugarcane leaf (SCL), Pineapple peel (PP), Banana leaf (BL), Sweet lime peel (SLP), Cheese whey permeate (CWP), Dry yeast cells (DYC), Pongamia seed cake (PSC), Jatropha seed cake (JSC) and Groundnut oil cake (GOC) as substrates were eened for iturin A production. All these substrates, except yeast cells and whey were collected freshly from local markets and farms, cleaned, sliced, dried at 60 °C, blended to fine powder and stored in air tight containers at room temperature. The dry yeast cells used were of commercial, granulated food grade yeast. Whey was collected from a local dairy industry, Hyderabad. All these raw materials were directly used as substrates in the fermentation media without any pre-treatment at 1 % level in place of glucose, inoculated with 1 % of 24 h culture (O.D- 0.6) and incubated at 37 °C at 180 rpm for 48 h.

see scr col

2.14. Selections of most suitable agro- industrial sources using Plackett-Burman design

Based on the results obtained from preliminary screening of agro-industrial wastes for Iturin A production, eight different agro-industrial wastes i.e., SOC, CSOC, COC, POC, JSC, CWP, DYC and GOC were selected and further screened by Plackett- Burman (PB) design. PB design is a two-level factorial design and allows the investigation of n-1 variables in n number of experiments (Plackett and Burman, 1946). In the experimental design, each row represents an experiment and each column represents an independent variable. For screening agro industrial wastes as substrates for Iturin A production, a set of 16 experiments were performed at combinations of '+' (high-0.2 %) and '-' (low-0.02 %) levels. Plackett-Burman experimental design was based on the first order model:

Y = ßo+XßiXi

Where, Y is the response (iturin A productivity), Po is the model intercept, pi is the variable estimates. Plackett-Burman design matrix was developed and the results were analysed using MINITAB-13 statistical software.

%) levels.

veloped !

2.15. Response surface methodolog

After selecting a single best substrate (agro-industrial waste) for iturin A production through preliminary experiments and PB design, response surface methodology (RSM) was employed to determine the optimum conditions for maximum iturin A production. Five variables, i.e., substrate concentration along with pH, temperature, inoculum size and incubation period were selected for optimization. Central composite rotatable design (CCRD) with five coded levels (-2, -1, 0, +1, +2) was performed in a design of 32 experiments with six replicates at the central point. The following equation was used for coding the actual experimental values of the variables (Neter et al., 1996).

Xi = (Xi-Xo)/AX

Where X; is dimension less coded level of the variable, X; is actual value of that variable, X0 is average of the high and low level values of that variable and AX is high value minus low value of that variable. Analysis of variance (ANOVA) for both iturin A production and

reduction in surface tension was done by MINITAB-13. The response (Iturin A mg/L and reduction in surface tension) was analyzed by using a second order polynomial equation and the data were fitted into the equation by multiple regression procedure. The three dimensional graphical presentation of model equation represents the individual and interactive effect of test variables on the response. The optimum levels of variables for high iturin A production as well as reduction in surface tension were obtained by solving the regression equation and analysis of response surface plots. The second-order polynomial equation that defines predicted response (Y) in terms of the independent variables (X1, X2, X3, X4 and X5) is given below:

Y = ßo+ßlXi+ß2X2+ß3X3+ß4X4+ß5X5+ßllX2i+ ß22X22+ß33XVß44XVß55XVßl2XlX2

+ßl3XlX3+ßl4XlX4+ßl5XlX5+ß23X2X3+ß24X2X4 +ß25X2X5

where P0 is intercept term, Pi, P2, P3, P4, P5 linear coefficients, P11, P22, P33, P44 quadratic coefficients and P12, P13,P45 interactive coefficient estimate. The optimum levels of variables (with in the experimental range) to obtain maximum Iturin A production and reduction in surface tension were determined by running experiment using the optimum values for variables given by response optimization for confirmation of predicted value.

2.16. Statistical analysis

)onse opt

perform

All experiments were performed in triplicates and the results obtained are mean of three independent experiments showing consistent results. ANOVA, Means, CV %, Ranking and standard errors were calculated using the Microsoft Office Excel 2003 (version 7).

3. Results

3.1. Isolation and preliminary characterization of biosurfactant producing Bacillus spp.

Rhizosphere soil samples collected from varied crop plants (chick pea, groundnut, millet, pigeon pea and sorghum) were subjected to enrichment culturing technique to select biosurfactant producers, and a total of 100 morphologically distinct bacterial isolates were obtained. Based on Gram's staining and spore staining, all the isolates were preliminarily identified as Bacillus spp. and were selected for further studies. The biosurfactant activity of

all the isolates was identified qualitatively by microplate and penetration assays. The microplate assay is performed based on the change in optical irregularity, pure water in a hydrophobic well shows a flat surface where as the fluid surface containing surfactants is concave and turns into an irregular shape. In penetration assay, it was observed that biosurfactant activity is indicated by change in colour from clear red to cloudy white within 15 min. Based on results obtained from microplate and penetration assays, twenty isolates

were selected for further studies.

r detectio

Oil spreading assay is a rapid and more sensitive method for detection of biosurfactants. All twenty isolates showed a clear zone on crude oil which varied from 25 to 40 mm in dia with RHNK22 having 40 mm (data not shown). All the twenty isolates showed poor growth on blue agar plates and produced no halos. This indicates that none of the isolates produced glycolipids. In blood haemolysis test, all the twenty isolates have showed zone of haemolysis that ranged 4 to 15 mm in dia with RHNK22 having 15 mm (data not shown). All the twenty isolates showed a clear halo around the colonies on tributyrin agar plates indicating lipase activity and the zone size ranged from 6 to 14 mm in dia (data not shown) and RHNK22 showed maximum zone size of 14 mm dia in size.

3.2. Emulsification index (EI %) and Emulsification activity (EA) of isolates

: (EI %) and

In the present study, emulsification index and emulsification assay were carried out with 9 different hydrocarbons like kerosene, benzene, diesel, coconut oil, sunflower oil, toluene, engine oil and petrol. Among twenty isolates, RHNK22 showed highest EI percentage (EI %) 78.1 on kerosene, 62.5 on benzene, 72 on diesel, 53.1 on coconut oil, 62.5 on sunflower, petrol and toluene, 68.7 on engine oil (red oil) and xylene (Table 1). In emulsification assay, the isolate Bacillus sp. RHNK22 showed highest emulsification activity [EA- emulsification units (Eu) / mL] 214.0 on kerosene, 93.1on benzene, 253.5 on diesel, 226.7 on coconut oil, 291.4 on sun flower oil, 265.1 on toluene, 210 on engine oil, 214.1 on xylene and 252.9 on petrol (Table 2). To our knowledge, this is the first observation of EI and EA with nine different hydrocarbons for screening bacteria for biosurfactant activity.

3.3. Surface tension (ST) measurement

Surface tension measurement was carried out for all the twenty selected isolates and it

was observed that RHNK22 was able to show highest reduction in surface tension from 60.50 to 26.12 mN/m in 24 h and 29.04 mN/m in 48 h in comparison with other isolates. Of 20 bacterial isolates, four isolates RHNK 1*, RHNK5*, RHNK22* and RHNK 30* showed significant p values less than 0.05. Significant change in surface tension values within 24 - 48 h was observed for the bacterial isolate RHNK22. Whereas with the other isolates showed ST values ranged from 60.39- 27.96 mN/m with in 24 h and 50.12 - 29.86 mN/m in 48 h (Fig.

3.4. Anti-fungal activity by dual culture method

Anti-fungal activity against phytopathogens, M. phaseolina and S. rolfsii was studied. Among 20 isolates, Bacillus sp. RHNK22 showed effective antifungal activity against tested fungi in three different media. The isolate RHNK22 exhib ited an inhibition (I %) of 76.9 on PDA, 80 on GCY, 72.5 on KB against M. phaseolina and 73.3 on PDA, 80 on GCY, 75.5 on KB medium against S. rolfsii (Fig. 2).

3.5. Identification of isolate Bacillus spp. RHNK22

Bacterial isolate RHNK22 was identified as Gram positive, rod shaped motile and sporulating bacterium. Biochemical tests revealed that the isolate was positive for indole test, Voges-Proskauer test, citrate utilization test, catalase test, starch hydrolysis and glucose fermentation and negative for methyl red test (Table 3). The 16S rRNA gene sequence results were obtained from a BLAST search of EzTaxon server and RHNK22 was identified as Bacillus amyloliquefaciens (Fig. 3).

3.6. Antifungal activity of the extraction Iturin A

In this study, the extracted biosurfactant (iturin A) at different concentrations was tested for antifungal activity. It was observed that iturin A produced by strain RHNK22 showed 77.7 % inhibition of the phytopathogens tested at 5 mg/mL concentration (Fig. 4).

3.7. PCR, HPLC and FTIR analysis for iturin A

Primers were used for the amplification of gene involved in the antibiotic biosynthesis from B. amyloliquefaciens RHNK22 in this study PCR product was amplified by using pri-

mers and showed 647bp of band corresponding to the iturin A gene (Fig. 5). HPLC analysis of the extracted surfactant of RHNK22 showed homolog peak with standard iturin A (Fig. 6). FTIR spectra of the extracted surfactant absorption valleys at 1521 and 1558 indicate that the compound contains peptide bonds. A lactone ring is suggested by the absorption at 1732 cm-1 and valleys that result from C-H stretching (1174, 1234, 1319, 1338, 1361, 1404 cm-1) indicate the presence of an aliphatic chain. Bands in the range 1570 to 1515 cm-1, resulting from the deformation mode of the N-H bond combined with C-N stretching mode (amide II band), both indicating the presence of a peptide component. Also C-H stretching was observed at 1730 cm-1 (data not shown). B. amyloliquefaciens RHNK22 produced iturin A which was confirmed by HPLC & FTIR.

3.8. Uni-dimensional screening of agro-wastes as substrates for iturin A production

Results of unidimensional screening of agro-industrial wastes as substrates are listed in (Table 4). The data on iturin A production using different agro-industrial wastes as substrates, at 48 h of incubation showed a wide variation from 103 to 276 mg/L. The top eight substrates (SOC, COC, CSOC, CWP, DYC, POC, JSC and GOC) which showed maximum iturin A production were selected for second level statistical screening.

ere selecte

/......'

3.9. Plackett-Burmann design for screening different agro-industrial wastes as substrates for iturin A production

0.0! var

Iturin A production varied from 360 mg/L to 780 mg/L among the sixteen runs of PB design (Table 5). On analysis of regression coefficient of eight variables SOC, CSOC, COC, JSC, CWP and GOC showed positive effects for iturin A production. Out of eight different substrates tested, three substrates (SOC, CWP & DYC) were found to be significantly affecting iturin A production with P values less than 0.05, of which SOC with a P value of 02 was found to be highly significant and was used for further studies (Table 6). The other variables, CSOC, COS, JSW, GOC and POC were found to be insignificant, with P values greater than 0.05, that is increase in their concentrations would not have any effect on iturin A production (Table 7).

3.10. Response surface methodology (RSM)

For optimization of iturin A production by B. amyloliquefaciens RHNK22 in submerged fermentation using SOC as substrate, RSM was employed. Five variables i.e., substrate (SOC) concentration, along with physical parameters like temperature, pH, inoculum size (IS) and incubation period (IP) were studied by central composite rotatable design (CCRD) at five levels and their interactions on iturin A production and reduction in surface tension were determined (Table 8). Thirty two trails or runs were performed to locate the optimum conditions for maximum iturin A production and reduction in surface tension. Both, highest iturin A production and reduction in surface tension were observed at run 6. The student't' distribution and corresponding values, along with parameter estimate are given in (Table 9 &10). The probability (P) values were used as a tool to check the significance of each of the coefficient.

tens con sign

For understanding the simultaneous influence of variables, regression analysis was performed to fit the response function with experimental data. Multiple regression analysis was used to analyse the data and thus a polynomial equation was derived from regression analysis for both iturin A production and reduction in surface tension as follows:

Y1=643.61 + 14.92 X1 -29.08 X2 -23.25 X3 - 47.0 X4 - 18.33 X5 - 5.24 X12 + 5.14 X22 -1.11 X32 - 11.11 X42 + 13.89 X52 - 12.63 X1 X2 + 3.63 X1 X3 -17.0X1X4 -8.75 X1X5 -25.25 X2X3 + 65.88X2X4 -2.88 X2X5 -11.12 5.65 X3X4 -4.62 X3X5 - 13.25 X4X5

Y2=31.112 -0.3313X1 +0.0286 X2+ 0.9279X3 - 0.2285X4 - 0.1104 X5 -0.0427X12 -0.2038X22 -0.6050X32 - 0.1644X42 + 0.1331Xs2 + 0.0830X1 X2 -0.3340 X1 X3 -0.2368X1X4 -0.9519X1X5 +0.4091X2X3 -0.9706X2X4 -0.1232 X2X5 + 0.3404X3X4 +0.5020 X3X5 - 0.1422

where Y1 and Y2 are the predicted responses of iturin A yield and the reduction in surface tension and X1, X2, X3, X4 and X5 were the coded values of the test variables SOC centration, temperature, pH, inoculum size and incubation period respectively. The significance of each coefficient determined by Student's t-test and P-values are given in (Table 9 & 10). Larger t-value and smaller P-value indicate a higher level of significance for the corresponding coefficient. In ANOVA for iturin A production, the P-value of each model term, the constants, X1; X2; X3; X4; X5; X24; X25 ; X1X2, X1X4, X2X3, X2X4, X4X5 were

found to be significant (P<0.05) and in the ANOVA for surface tension reduction X1, X3, X4, X22, X23, X1X3, X1X5, X2X3, X2X4, X3X4, X3X5 were found to be significant.

The results of the second-order response surface model fitting in the form of ANOVA are given in (Table 11 & 12). F-test for regression was significant at 5 % level. The ANOVA indicates that the effect of interactions of variables, quadratic effects and regression between independent variables are quite significant (Table 11 & 12). To test the fit of the model equation, regression-based determination coefficient R2 was evaluated. The nearer the values of R2 to 1, the model would explain better for variability of experimental values to the predicted values. The model presented a high determination coefficient (R2 = 0.97) explaining 97% of the variability in response and only 3% of total variations could not be explained by the model. The value of adjusted determination coefficient (Adj R2 = 0.92) was also very high to reflect a good fit between the observed and predicated responses. The present analytical results indicated that the response equation provided a suitable model for process optimization of iturin A production.

ten wit low

Response surface plots were generated to show the interaction of each pair of variables on iturin A production and reduction in surface tension (Fig. 7). Each figure presented the interactive effects of two variables on the yield by keeping the other variables at their middle levels, exhibiting a visual interpretation of the location of optimum experimental conditions. An elliptical contour plot indicated a significant interaction between variables. The results show that, among the independent variables, all the variables tested have significant effect on iturin A production, however, substrate concentration, incubation period and pH were found to have significant effect on reduction in surface tension. In square terms, pH-pH and temperature-temperature were significant for iturin A production and IS-IS and IP-IP were significant for reduction in surface tension. Among the interactions, substrate concentration-IS, substrate concentration-pH, IS-IP, IS-pH and pH-temperature were significant for iturin A production (Fig. 7). High iturin A production and reduction in surface tension could be observed with higher concentration of substrate up to a limit (4 %) and also h higher incubation period at hold values of other respective parameters. As per (Fig. 7), lower IS and higher IP lead to higher iturin A production and reduction in surface tension. Maximum iturin A production and reduction in surface tension could be observed at higher concentration of substrate (4 %), 1 % IS, 48 h-IP, pH-6.0 and an incubation temperature of 37+2° C.

4. Discussion

Biosurfactants are a heterogeneous group of secondary metabolites having surface active properties which are known to be produced by a variety of microorganisms. The biosurfactants possess unique properties like higher biodegradability (Lovaglio et al., 2011), lower toxicity (Chtioui et al., 2010) and greater stability, which make them use as alternative for chemical surfactants, for the applications in refinery industry, agriculture, pharmaceuticals, cosmetics, plastic, textile, food and machinery fields (Ongena and Jacques, 2008; Mukherjee et al., 2006; Edwards et al., 2003; Liu et al., 2010; Mulligan 2005). The present study was conducted to screen for biosurfactant producing bacteria with potential antagonistic activity against phytopathogens, characterisation of the biosurfactant and development of a cost-effective medium formulation for biosurfactant production

Of the different rhizosphere bacterial isolates characterized for biosurfactant and

antifungal activity, RHNK22 isolated from groundnut rhizosphere was potential. Rhizosphere

microorganisms are known to produce biosurfactants which play a key role in plant-microbe

interactions (Sachdev and Cameotra, 2013). Due to diverse chemical nature and mode of

action of biosurfactants, screening for their activity by a single method is not valid and it is necessary to perform more than one screening methods for isolating bacteria with potential biosurfactant activity (Youssef et al., 2004). In this study, nine different screening methods were used for selecting biosurfactant producer from rhizosphere. Plaza et al. (2006) reported that the area of oil spreading zone depends on the concentration of biosurfactant, the isolate RHNK22 showed highest zone (40 mm in dia) [data not shown] compared to the other isolates in this study. Blue agar plate method is a semi-quantitative method to detect anionic surfactants and other extracellular glycolipids (Siegmund and Wagner, 1991). In the present study, as none of the isolates formed blue colored zones on CTAB agar plates, it was confirmed that the biosurfactant produced by all the twenty bacterial isolates were not

glycolip

ids and anionic nature.

Camilios-Neto et al. (2011) found an association between haemolytic activity and biosurfactant production and they recommended the use of blood agar lysis as a primary method to screen for biosurfactant production. However, not all bacteria with haemolytic activity are biosurfactant producers, as compounds other than biosurfactants can also cause haemolysis (Youssef et al., 2004). Surfactants with lipase activity have good interfacial activity on water-oil surfaces (bioemulsifiers) (Kokare et al., 2007). In our observation, twenty

isolates have good haemolytic and lipase activities. Previous reports of Satpute et al. (2008); Amiriyan et al. (2004) suggest EI and EA methods are essential methods to screen for potential biosurfactant producers and a study on Bacillus subtilis MTCC 2422 exhibited an EI % of 68 with kerosene and emulsification assay on petrol 291.4 (EU/mL). In this study, we have tested for EA and EI with nine different hydrocarbon oils and surfactant produced by the isolate RHNK22 showed promising EI and EA, which varied with different hydrocarbons (Table 1 & 2). Das et al. (2009) reported that an efficient biosurfactant can reduce the surface tension of water to <35 mN/m. In this study, we observed that isolate RHNK22 has shown 26.12 mN/m reduction in surface tension of water within 24 h (Fig. 1).

Soil borne plant pathogens such as Sclerotium rolfsii and Macrophomina phaseolina cause collar rot and charcoal rot disease respectively in crop plants and are mostly managed by chemical pesticides which may lead to development of resistant pathogens and can also affect symbiotic soil microflora apart from soil fertility. Moreover, such chemicals can enter the food chain and accumulate as undesirable chemical residues (Kim et al., 2004). Lipopeptide antibiotics produced by Bacillus spp are known to inhibit phytopathogenic fungi (Cao et al., 2009; Chen et al., 2009). Singh et al. (2014) have reported that the antifungal activity of iturin A against is due to membrane permeabilization properties. Murata et al., (2013) have reported the inhibition of phytopathogenic fungi, Rhizoctonia solani with an in-

hibition of 80 % by iturin A produced by B. amyloliquefaciens on PDB medium. In the present study, we have tested the antifungal activity of B. amyloliquefaciens RHNK22 for both cell-mediated and extracted iturin A against phytopathogens M. phaseolina and S. rolfsii by dual culture method on 3 different media. RHNK22 showed inhibition of 80 % and the extracted iturin A showed 77.7 % at 5 mg/mL concentration (Fig. 2 & 4). Bacterial isolate RHNK22 was characterized by the morphological, biochemical characteristics and 16S rRNA gene sequence and identified as Bacillus amyloliquefaciens RHNK22 (Table 3, Fig. 3). According to Chun et al. (2007) all the results were obtained from BLAST search EzTaxon server to determine the exact nomenclature of isolate. Presence of iturin A gene in RHNK22 was confirmed by PCR amplification with specific primers and the biosurfactant produced was identified as iturin A (Fig. 5 & 6) by HPLC according to method reported by Murata et al. (2013). FTIR analysis of the extracted biosurfactant revealed the presence of carboxyl group and peptide component which was in accordance with the results reported by Ramaratham et al. (2007).

The use of cheaper substrates (oil cakes, molasses, distillery wastes, starchy materials, cheese whey, etc) obtained as by-products from various agro industrial sectors have been reported as carbon and nitrogen sources for biosurfactant production (Makkar and Cameotra, 2002). Oil cakes obtained as by-product after oil extraction from seeds is mainly composed of proteins, fibres, crude lipids and minerals, can act as nutrients source for microbial growth and secondary metabolite production. In 2014 the production of SOC was estimated at about 220 million tons (http://www.indexmundi.com). In the present study, an initial screening of different agro-industrial wastes as substrates for biosurfactant production was done to understand the significance of their effect on product formation. A few better ingredients were selected for further level of statistical screening by Plackett-Burman design and the optimum levels of the selected substrates were optimized by RSM.

Previous studies revealed that, using rapeseed meal iturin A production was 600 mg/L in submerged fermentation conditions. However, reports on SOC for biosurfactant production are meagre and study by Jadhav et al. (2011) revealed that Enterobacter sp. showed 1.5 g/L of glycolipid production. In this study, using SOC, iturin A production with RSM showed 3 fold increase (819 mg/L) when compared with mineral salt medium. Sunflower oil cake consists of 58.25 % carbon, 5.88 % nitrogen 36.76 % protein and crude lipid 1.15 % (Lomascolo et al., 2012). The exact reason for enhanced biosurfactant production by SOC cannot be pointed; however, it could be possible because of the high lipid content, free amino acids, and soluble protein present in SOC. The lowest yield of iturin A (103 mg/L ) was obtained using orange peel as substrate which might be attributed to absence of lipid content, though carbon 41.25% and nitrogen 1.28% are present (http://nutritiondata.self.com; Gnaneshwar et al., 2013).

5. Con

bio the

5. Conclusion

In present study, 100 Bacillus spp. from rhizosphere soil samples were screened for osurfactant activity using various basic and high-throughput screening methods. Among them, one bacterial isolate identified as B. amyloliquefaciens RHNK22 isolated from groundnut rhizosphere showed significant biosurfactant activity and reduction of surface tension. Biosurfactant, identified as iturin A by FTIR and HPLC analysis and it had potential antifungal activity. Using RSM model, sunflower oil cake showed 3 fold increases in iturin A production and in view this it can be recommended for large scale production, to meet the demand of global requirement of biosurfactants.

Acknowledgements

PNK acknowledges the support in the form of research fellowship [UGC- RGNF-award no

F.14-2(SC)/2010(SA-III)].

References

Amiriyan, A., Mazaheri Assadi, M., Sajadian, V.A., Noohi, A.A., 2004. Bio emulsan production by Iranian oil reservoirs microorganism. Iranian J. Env. Health. Sci. Eng. 1, 28-35.

Anandaraj B, Thivakaran P (2010). Isolation and production of biosurfactant producing organism from oil spilled soil. J. Biosci. Technol. 1, 120-126.

Camilios-Neto, D., Bugay, C., de Santana-Filho, A.P., Joslin, T., de Souza, L.M., Sassaki, G.L., Mitchell, D.A., Krieger, N., 2011. Production of rhamnolipids in solid-state cultivation using a mixture of sugarcane bagasse and corn bran supplemented with glycerol and soybean oil. Appl. Microbiol. Biotechnol. 89, 1395-1403.

Cao, X.H., Liao, Z.Y., Wang, C., Yang, W.Y., Lu, M.F., 2009. Evaluation of a lipopeptide biosurfactant from Bacillus natto TK-1 as a potential source of anti-adhesive, antimicrobial and antitumor activities. Braz. J. Microbiol. 40, 373-379.

Chen, H., Wang, L., Su, C.X., Gong, G.H., Wang, P., Yu, Z.L., 2009. Isolation and characterization of lipopeptide antibiotics produced by Bacillus subtilis. Lett. Appl. Microbiol. 47, 180-186.

Chtioui, O., Dimitrov, K., Gancel, F., Nikov, I., 2010. Biosurfactants production by immobilized cells of Bacillus subtilis ATCC 21332 and their recovery by pertraction. Process. Bi chem. 45, 1795-1799.

Chun, J., Lee, J.H., Jung, Y., Kim, M., Kim, S., Kim, B.K., Lim, Y.W., 2007. EzTaxon: a web-based tool for the identification of prokaryotes based on 16S ribosomal RNA gene sequences. Int. J. Syst. Evol. Microbiol. 57, 2259-2261.

, D.G., Zajic, J.E., 1980. Surface-active compounds from microorganisms. Adv. Appl. icrobiol. 26, 229-253.

Das, P., Soumen, M., Ramkrishna, S., 2009. Substrate dependent production of extracellular biosurfactant by a marine bacterium. Biores. Technol. 100, 1015-1019.

Dhanya, G., Swetha, S., Madhavan, N.K., Sukumaran, R.K., Pandey Ashok., 2008. Response surface methodology for the optimization of alpha amylase production by Bacillus amyloliquefaciens. Bioresour. Technol. 99, 4597-4602.

Dhouha, G., Lobna, A., Ines, M., Radhouan, K., Imen, A., Imen, S., Sameh, M., Semia, Chaabouni., 2012. Investigation of antimicrobial activity and statistical optimization of Bacillus subtilis SPB1 biosurfactant production in solid-state fermentation. J. Biomed. Biotechnol. 373682.

Dubey, K., Juwarkar, A., 2001. Distillery and curd whey as viable alternative sources for biosurfactant production. World. J. Microbiol. Biotechnol. 17, 61-69.

Edwards, K.R., Lepo, J.E., Lewis, M.A., 2003. Toxicity comparison of biosurfactants and synthetic surfactants used in oil spill remediation to two estuarine species. Mar. Pollut. Bull. 46, 309-1316.

Gnaneshwar Goud, K., Chaitanya, K., Gopal Reddy., 2013. Enhanced production of P-D-fructofuranosidase by Saccharomyces cerevisiae using agro-industrial wastes as substrates. Biocatal. Agric. Biotechnol. 2, 385-392.

Ikram, U.H., Ali, S., 2005. Invertase production from a hyper producing Saccharomyces cerevisiae strain isolated from dates. Pakistan. J. Botany.37, 749-759.

Jin, H., Zhang, X., Li, K., Niu, Y., Guo, M., Hu, C., Wan, X., Gong, Y., Huang, F., 2014. Direct bio-utilization of untreated rapeseed meal for effective iturin A production by Bacillus subtilis in submerged fermentation. PLoS ONE. 9: 10

Kim, P.I., Bai, H., Bai, D., Chae, H., Chung, S., Kim, Y., 2004. Purification and characterization of a lipopeptide produced by Bacillus thuringiensis CMB26. J. Appl. Microbiol. 97, 942-949.

Kokare, C.R., Kadam, S.S., Mahadik, K.R., Chopade, B.A., 2007. Studies on bioemulsifier production from marine Streptomyces sp. S1. Ind. J. Biotechnol. 6, 78-84.

Laxman, R.S., Sonawane, A.P., More, S.V., Rao, B.S., Rele, M.V., Jogdand, V.V., Deshpande,V.V., Rao, M.B., 2005. Optimization and scale up of production of alkaline protease from Conidiobolus coronatus. Process. Biochem. 40, 3152-3158.

Lomascolo, A., Uzan-Boukhris, E., Sigoillot, J, C., Fine, F., 2012. Rapeseed and sunflower meal: a review on biotechnology status and challenges. Appl. Microbiol. Biotechnol. 95, 1105-1114.

Liu, Z.F., Zeng, G.M., Wang, J., Zhong, H., Ding, Y., Yuan, X.Z., 2010. Effects of monorhamnolipid and Tween 80 on the degradation of phenol by Candida tropicalis. Process. Biochem. 45, 805-809.

Lovaglio, R.B., dos Santos, F.J., Junior, M.J., Contiero, J., 2011. Rhamnolipid emulsifying activity and emulsion stability: pH rules. Colloids. Surf. B. Biointerfaces. 85, 301-305.

Makkar, R.S., Cameotra, S.S., 2002. An update on the use of unconventional substrates for

biosurfactant production and their new applications. Appl. Microbiol. Biotechnol. 3, 428-434.

Meena, K.R., Kanwar, S.S., 2015. Lipopeptides as the antifungal and antibacterial agents: applications in food safety and therapeutics. Bio. Med. Research. International. Article ID 473050: 9.

Mital, J., Anuradha, K., Sheetal, J., Sanjay, G., 2011. Isolation, characterization and antifungal application of a biosurfactant produced by Enterobacter sp. MS16. Eur. J. Lipid Sci. Technol. 113, 1347-1356.

Mukherjee, S., Das, P., Sen, R., 2006. Towards commercial production of microbi al surfactants. Trends. Biotechnol. 24, 509-515.

Mulligan, C.N., 2005. Environmental applications for biosurfactants. Environ. Pollut. 133, 183-198.

Murata, D., Sawano, S., Ohike, T., Okanami, M., Ano, T., 2013. Isolation of antifungal bacteria from Japanese fermented soybeans, natto. J. Environ. Sci. 25, 127-131.

Neter, J., Kutner, M.H., Nachtsheim, C.J., Wasserman, W., 1996. Applied Linear Statistical Models, fourth ed. McGraw-Hill, New York.

Nitschke, M., Pastore, G.M., 2006. Production and properties of a surfactant obtained from Bacillus subtilis grown on cassava wastewater. Biores. Technol. 97, 336-341.

Ongena, M., Jacques, P., 2008. Bacillus lipopeptides: versatile weapons for plant disease biocontrol. Trends. Microbiol. 16, 115-125.

Plackett, R.L., Burman, J.P., 1946. The design of optimum multifactorial experiments. Biometrika. 33, 305-325.

Plaza, G.A., Zjawiony, I., Banat, I.M., 2006. Use of different methods for detection of thermophilic biosurfactant-producing bacteria from hydrocarbon-contaminated and bioremediated soils. J. Pet. Sci. Eng. 50, 71-77.

Radhika, C., Yao, J., Masakorala, K., Jain, A.K., Kumar, R., 2014. Enhanced production and characterization of biosurfactant produced by a newly isolated Bacillus amyloliquefaciens USTBb using response surface methodology. Int. J. Curr. Microbiol. App. Sci. 3, 66-80.

Ramaratham, R., Bo, S., Chen, Y., Fernando, W.G., Xuewen, G., de Kievit, T., 2007. Molecular and biochemical detection of fengycin and bacillomycin D-producing bacillus spp., antagonistic to fungal pathogens of canola and wheat. Can. J. Microbiol. 53, 901-911.

Romero, D., Vicente, A., Olmos, J.L., Davila, J.C., Perez-Garcia, A., 2007. Effect of lipopeptides of antagonistic strains of Bacillus subtilis on the morphology and ultra struc-

ture of the cucurbit fungal pathogen Podosphaera fusca. J. Appl. Microbiol. 103, 969-

Sachdev, D.P., Cameotra, S.S., 2013. Biosurfactants in agriculture. Appl. Microb. Biotechnol. 97, 1005-1016.

Satpute, S.K., Bhawsar, B.D., Dhakephalkar, P.K., Chopade, B.A., 2008. Assessment of different screening methods for selecting biosurfactant producing marine bacteria. Indian. J.

Siegmund, I., Wagner, F., 1991. New method for detecting rhamnolipids extracted by pseudomonas species during growth on mineral agar. Biotechnol. Tech. 5, 265-268. Singh, A.K., Rautela, R., Cameotra, S.S., 2014). Substrate dependent in vitro antifungal activity of Bacillus spp strain AR2. Microb. Cell. Fact. 13: 67.

Sneath, P.H.A., Mair, N.S., Sharpe, M.E., Holt, J.G., 1986. Bergey's Manual of systematic

bacteriology, Volume 2, Baltimore, USA: Williams and Wilkins Vaux, J.D., Cottingham, M., 2001. Method and apparatus for measuring surface configuration. Patent number: US7224470 B2. Yarchoan, M., Arnold, S.E., 2014. Repurposing diabetes drugs for brain insulin resistance in

Alzheimer Disease. Diabetes. 63, 2253-2261. Youssef, N.H., Duncan, K.E., Nagle, D.P., Savage, K.N., Savage, K.N., Knapp, R.M., Mclnerney, M.J., 2004. Comparison of methods to detect biosurfactant production by diverse microorganisms. J. Microbiol. Methods. 56, 339-347.

Mar. Sci. 37, 243-250.

Fig. 1: Surface tension measurement of the different biosurfactant producing bacterial isolates

Fig. 2: Antifungal activity of biosurfactant produing bacterial isolates

(A) Control (Sclerotium rolfsii) (B) Antifungal activity of bacterial iso against Sclerotium rolfsii on PDA medium

(C) Antifungal activity of biosurfactant produing bacterial isolates against Sclerotium rolfsii. C 30

KliLzosj) ln-rc i.vulalrü

(D) Control (Macrophomina phaseolina) (E) Antifungal activity of bacterial isolate RHNK22 against Macrophomina phaseolina on PDA medium

(F) Antifungal activity of biosurfactant producing bacterial isolates against Macrophomina phaseolina

Fig. 3: Identification of Bacillus sp RHNK22 using 16S rRNA sequencing and construction of phylogenetic tree

— Bacillus tequilensis

- zZJH600280zZ

— Bacillus amyloliquifaciens subsp. amyloliquifaciens -Bacillus aerius

Bacillusamyloliquefaciens RHNK22 -Bacillus aerophilus

Bacillus shackletonii

Bacillus circulans

zZJF824810zZ

Bacillus salsus

Fig. 4: Antifungal activity of extracted lipopeptide (iturin A)

(A) Inhibition of Sclerotium rolfsii on PDA medium using different concentrations of Iturin A

(B) Inhibition of Macrophomina phaseolina on PDA medium using different concentrations of iturin A

A and B showed inhibition on phytopathogens with different concentration levels of extracted Iturin A (mg/mL) [A=0.07, B=0.15, C=0.31, D=0.62, E=1.25, F=2.5, G=5.0 and

W = control (water)]

Sclerotium rolfsii at different concentrations of iturin A produced by B.

amyloliqu efciciens RHNK22

0.07 0.15 0.31 0.52 1.25 2.5 Concentration of iturin A (mg;ml)

(D) Inhibition of Macrophomina phaseolina at different concentrations of iturin A produced

by B. amyloliquefaciens RHNK22.

Fig. 5: PCR product of amplified iturin A gene from B. amyloliquefaciens RHNK22

M: DNA marker

P: Iturin A gene specific primers designed by Ramaratham et al. (2007) T: Primers designed in this study

Fig. 6: (A) HPLC peak of Standard Iturin A (sigma chemical).

(B) HPLC peak of itutin A from B. amyloliquefaciens RHNK22

Fig. 7: Response surface plots of iturin A production and surface tension (ST) by B. amyloliquefaciens RHNK22.

A-E are Response surface plots of iturin A production, in terms of interaction between (A) inoculum size level and pH, (B) substrate concentration and inoculums size, (C) substrate concentration and pH, (D) inoculum size level and incubation period and (E) pH and temperature.

F- K are Response surface plots of reduction of surface tension, in terms of interaction between (F) substrate concentration and incubation period, (G) inoculum size level and incubation period, (H) substrate concentration and temperature, (I) incubation period and temperature, (J) incubation period and pH and (K) inoculums size and pH

Table 1

Emulsification Index (EI %) of biosurfactant producing bacterial isolates using different hydrocarbons

S. Isolate No label

Kerosene

Benzene

Diesel

Coconut oil Sun flower

Toluene Engine oil

Xylene

Petrol

10 11 12

R5 R8 R10 R14 R16 R19 R20 R22 R30 R33 R40 R44 R48 R52 R61 R66 R72 R85 R91

31.2c(±0.08)

62.5a(±1.09) 62.4a(±1.11) 62.5b(±1.09) 15.4g(±1.08) 31.2c(±1.08) 56.2a(±1.08) 56.2a(±1.09) 78.1a(±1.08) 56.2b(±1.08) 71.8a(±1.09) 12.5f(±1.08) 31.2f(±0.01) 15.6d(±1.10) 31.2e(±1.10) 15.6e(±1.08) 15.6f(±0.01) 71.8a(±1.09) 68.7a(±1.09) 46.8d(±0.01)

EEAI %

12.5f(±1.09)

50.0d(±1.13) 31.2e(±1.10) 56.2d(±1.08) 31.2d(±0.01) 15.6f(±1.10) 43.7d(±0.06) 46.8c(±0.01) 62.5d(±1.10) 12.5g(±1.10) 50.0d(±1.13) 15.6e(±1.10) 43.7c(±1.10) 56.2a(±1.11) 31.2e(±1.08) 18.7d(±0.01) 25.2d(±0.14) 50.0d(±1.13) 43.7g(±1.08) 31.2g(±1.09)

37.5b(±0.06)

62.5a(±1.09) 53.1c(±2.22) 65.6a(±2.19) 25.2e(±0.17) 24.9d(±1.07) 56.2a(±1.09) 50.0b(±1.13) 71.8b(±1.08) 56.2b(±1.09) 56.2c(±1.09) 15.6e(±1.09) 31.2f(±0.01) 15.6d(± 1.10) 18.7f(±1.09) 18.7d(±0.01) 15.6f(± 1.11) 62.5b(±1.09) 65.6b(±2.19) 53.1b(±2.22)

15.6e(±1.09)

53.1c(±2.22) 15.6f(±1.08) 43.7g(±1.09) 53.1b(±2.22) 46.8a(±0.01) 40.6e(±0.01) 37.5e(±0.01) 53.1e(±2.22) 46.8c(±0.01) 43.7e(±1.09) 46.8b(±0.01) 56.2a(±1.09) 15.6d(±1.08) 56.2a(± 1.11) 53.1a(±2.22) 40.6c(±0.04) 50.0d(±1.13) 50.0f(± 1.11) 53.1b(±2.22)

37.5b(±0.01)

40.6f(±0.01) 53.1c(±2.22) 56.2e(±1.09) 46.8c(±0.002) 31.2c(±0.01) 46.8c(±0.002) 43.7d(±1.09) 62.5d(±1.11) 37.5d(±0.04) 40.6f(±0.01) 40.6c(±0.06) 46.8b(±0.01) 56.2a(±1.09) 43.7c(±1.09) ^50.0b(±1.13) 43.7b(±1.09) 56.2c(±1.08) 50.0f(±1.13) 50.0c(±1.11)

24.9d(±1.07)

15.6g(±1.09) 59.3b(±1.13) 59.3c(±0.01 18.7f(±0.0 21.8e(±0.01) 53.1b(±2.22) 46.8c(±0.04) 62.5d(±1.09) 24.9f(±1.07) 56.2c(±1.09) 6.23g(±0.01) 40.6d(±0.05) 15.7d(±1.02) 47.4b(±0.57) 50.0b(±1.13) 15.6f(± 1.11) 56.3c(±1.10) 47.6f(±0.51) 35.4f(±0.69)

50.0a(±1.13)

44.6e(±1.34) 58.1b(±0.57) 0.7f(±1.28) "(±1.31) 6 .2b(±0.61) 45.0c(±0.67) 44.2d(±1.17) 68.9c(±1.11) 63.3a(±1.29) 50.4d(±1.19) 46.8b(±0.01) 43.7c(±1.08) 40.6b(±0.09) 56.2a(±1.09) 40.6c(±0.01) 53.1a(±2.22) 62.5b(±1.09) 53.4e(±2.31) 59.3a(±1.13)

24.9d(±1.07)

56.2b(±1.09) 62.5a(±1.08) 62.5b(±1.09) 15.6g(±0.01) 10.1g(±0.10) 56.2a(±1.09) 18.7f(±1.08) 68.7c(±1.09) 31.2e(±1.08) 62.5b(±1.09) 31.2d(±1.08) 56.2a(±1.10) 37.5c(±0.01) 37.5d(±0.01) 15.6e(±0.01) 15.6f(±0.01) 56.2c(±1.09) 62.5c(± 1.11) 59.3a(±1.13)

31.2c(± 1.09)

56.2b(±1.11) 46.8d(±0.02) 56.2e(±1.09) 31.2d(±1.09) 25.2d(±0.17) 43.7d(±1.09) 50.0b(±1.13) 62.5d(±1.09) 37.5d(±0.01) 43.7e(±1.09) 50.0a(±1.13) 37.5e(±0.01) 37.5c(±0.01) 15.6i(±0.01) 15.6e(±0.01) 15.6f(±0.02) 46.8e(±0.01) 59.3d(±1.13) 43.7e(±1.09)

4.62 4.78 3.91 3.94 3.46 3.30 3.42 3.72

3.70 3.29 4.17 2.32 4.52 4.25 4.07 4.77 3.45 4.66 4.96

Values superscribed by a-g are ranking highest to lowest of difference test (p<0.05). Values in the brackets are standard values in last column are CV %.

significant, same alphabet are insignificant according to Fischer's least significance error, values in column are mean two independent experiments of 4 replications and

Table 2

Emulsification activity (EA; EU/mL) of biosurfactant producing bacterial isolates using different hydrocarbons_

S. Isol Kerose Benzen N ate ne e

o lab el

Diesel Cocon Sun Toluen Engine Xylene Petrol ut oil flower e oil

14 8R 15

16 R6 1

18 R7 2

19 R8 5

100.3 ±0.01 181.8 ±0.07 192.6 ±0.05

193.0 ±0.14 98.9(± 0.05)

166.3 ±0.07

211.4 ±0.12

203.1 ±0.01 214.0 ±0.14

201.2 ±0.07 208.6 ±0.08

158.3 ±0.01 194.8 ±0.11

175.5 ±0.25 148.5 ±0.14

210.4 ±0.12 135.2 ±0.01 179.2 ±0.01 201.0 ±0.10

EA(E U/mL)

75.0(±0

.05) 85.1(±0

.07) 86.7(±0

.04) 90.1(±0

.07) 90.1(±0

.03) 92.5(±0

.04) 90.1(±0

.25) 90.1(±0

.07) 93.1(±0 .28) 83.2(±0

.14) 85.7(±0 .08) 88.4(±0

.07) 90.3(±0 .08) 91.2(±0 .02) 91.0(±0

.07) 84.5(±0

.05) 57.2(±0

.05) 93.6(±0

.07) 89.1(±0 .08)

EA(E EA(E U/mL) U/mL)

193.1(± 0.24) 226.4(± 0.07) 232.2(± 0.07) 235.7(± 0.04) 200.0(± 0.35) 235.2(± 0.07) 204.0(± 0.12) 241.0(± 0.35)

253.5(± 0.07)

238.5(± 0.10) 247.3(± 0.07) 144.8(± 0.09) 185.4(± 0.04) 216.6(± 0.49) 213.0(± 0.49) 248.6(± 0.07) 172.1(± 0.33) 204.6(± 0.03) 205.8(± 0.07)

205.5(± 0.03) 215.0(± 0.07) 218.3(± 0.08) 214.7(± 0.07) 209.5(± 0.03) 224.1(± 0.07) 222.7(± 0.07) 222.2(± 0.07) 226.7(± 0.07)

226.9(± 0.08) 226.2(± 0.07) 219.2(± 0.07) 231.6(± 0.10) 203.7(± 0.07) 189.1(± 0.33) 223.8(± 0.07) 213.2(± 0.08) 227.5(± 0.07) 222.7(± 0.11)

EA(E U/ m L)

215.1(± 0.07) 216.4(± 0.10) 215.1(± 0.07) 210.6(± 0.10) 215.3(± 0.08) 213.4(± 0.07) 219.8(± 0.03) 217.3(± 0.08)

291.4(± 0.10)

216.0(± 0.07) 218.4(± 0.10) 220.4(± 0.07) 218.1(± 0.10) 215.7(± 0.07) 218.2(± 0.08) 214.6(± 0.04) 213.9(± 0.07) 219.8(± 0.08) 216.4(± 0.07)

EA(E EA(E EA(E EA(E U/mL) U/mL) U/mL) U/mL)

207.4(± 0.07) 223.5(± 0.08) 227.3(± 0.07) 232.0(± 0.31) 232.6(± 0.05) 236.9(± 0.07) 238.8(± 0.07) 242.2(± 0.37) 265.1(± 0.20)

244.0(± 0.31) 251.4(± 0.04) 237.4(± 0.21) 247.0(± 0.56) 253.9(± 0.16) 259.2(± 2.85) 256.3(± 0.67) 251.2(± 0.24) 255.0(± 0.29) 262.3(± 0.55)

151.9(± 0.99) 203.4(± 0.07) 211.9(± 0.21) 206.3(± 0.67) 172.5(± 0.72) 144.2(± 1.30) 192.1(± 1.92) 208.4(± 1.32)

210.0(± 0.70)

200.2(± 0.04) 203.9(± 0.21) 136.5(± 0.09) 139.0(± 0.21) 191.4(± 0.07) 196.2(± 0.04) 163.7(± 0.07) 130.1(± 0.34) 118.9(± 0.05) 213.9(± 0.42)

186.7(± ^0.04) 204.8(± 0.09) 207.7(± 0.10) 203.4(± 0.07) 204.4(± 0.08) 201.6(± 0.08) 201.6(± 0.08) 207.1(± 0.08)

214.1(± 0.07) 204.7(± 0.08) 213.2(± 0.07) 187.9(± 1.07) 171.5(± 0.10) 213.8(± 0.07) 213.7(± 2.54) 209.5(± 0.08) 105.8(± 0.05) 199.8(± 0.08) 209.8(± 0.04)

181.3(±

0.08) 190.4(± 0.07) 161.6(± 0.07) 162.4(± 0.08) 132.5(± 0.07) 186.9(± 0.08) 175.7(± 0.12) 231.0(± 0.55)

252.9(± 0.07) 204.1(± 0.04) 221.0(± 0.15) 138.3(± 0.06) 158.9(± 0.11) 219.8(± 0.08) 169.6(± 0.08) 204.9(± 0.07) 172.5(± 0.08) 238.6(± 0.07) 224.3(± 0.08)

20 R9 103.6( 91.0(±0 249.0(± 226.2(± 215.3(± 263.8(± 164.9(± 200.1(± 114.4(± 1 ±0.18) .22) 0.39) 0.07) 0.07) 1.92) 0.43) 0.08) 0.05)

EA: (EU/mL) = 1 Emulsification unit = 0.01 O.D multiplied by dilution factor of absorbance at 400nm.Values in the brackets are standard error; values in column are mean two independent experiments of 4 replications.

Table 3

Morphological & biochemical identification of the bacterial isolate RHNK22

Morphological & Result

Biochemical tests

Gram staining Gram +ve

Motility +ve

Endo spore +ve

Starch hydolysis +ve

Indole +ve

Methyl red -ve

Voges Proskauer +ve

Citrate utilization +ve

Catalase +ve

Glucose fermentation +ve

Table 4

Uni-dimensional screening of different agro-industrial wastes as source for iturin A production by B. amyloliquefaciens RHNK22

Substrate

Iturin A (mg/ L) P value

Rice bran husk Sunflower oil cake * Coconut oil cake * Cotton seed oil cake * Corn cob Orange peel Jack fruit peel Sugarcane leaf Pine apple peel Banana leaf Sweet lime peel Cheese Whey * Dry yeast cells* Pongamia seed cake* Jatropha seed cake* Groundnut oil cake* Glucose with MSM

177 ±(1.06) 276 ± (1.08) 253 ± (1.70) 263 ± (1.03) 135 ± (1.63) 103 ± (1.03)

178 ± (1.08) 211 ± (0.86) 177 ± (0.64) 197 ± (0.89) 183 ± (1.08) 268 ± ( 253 ± (1.08)

1 ± (1.08) 238 ± (1.02) 258 ± (1.08) 270 ± (1.06)

0.552 0.019 0.013 0.019 0.105 0.051 0.105 0.105

05 0.591 0.420

_,o8) r

3 ± (1.08

0.105 0.029 0.029 0.019 0.019 0.019 0.105

Iturin A production by Bacillus amyloliquefaciens RHNK22 (mg/L). Values in the brackets are standard error; values in column are mean values of two independent experiments of 4 replications and * indicating significant (less than 0.05 of the P value) for production.

Table 5

The Plackett-Burman experimental design matrix: Screening agro-industrial waste for Iturin A production by B. amyloliquefaciens RHNK22

CSOC COC

CWP DYC

ITURIN

► 560

10 11 12

+ + + +

+ + + +

+ + + +

+ + + +

Number of runs-16, variables-08 » and centre points

es-08 and

Table 6

Estimated effects and coefficients for iturin A production (coded units): Plackett- Burmann design

Effect Coefficient SE Coefficient

Significance

Constant

157.00 69.00 23.00 -13.00 47.00 113.00 -109.00 39.00

565.50 78.50 34.50 11.50 -6.50 23.50 56.50 -54.50 19.50

15.71 15.71 15.71 15.71 15.71 15.71 15.71 15.71 15.71

35.99 5.00 2.20 0.73 -0.41 1.50 3.60 -3.47 1.24

0.000 0.002 0.064 0.488 0.691 0.178 0.009

Significant

SE, Standard error; t, Student t value; P, Probability, *Insig

Table 7

Analysis of variance for iturin A production (coded units): Plackett-Burmann design

Source DF Seq SS Adj SS Adj MS F P

Main Effects 8 233952 233952 29244 741 0.008

Residual Error 7 27644 27644 3949

Total_15 261596_

DF, degrees of freedom; Seq.SS, Sum of squares; Adj.SS, adjusted sum of squares; Adj. MS, adjusted sum of squares; F, Variance ratio; P, Probability

Table 8

Central composite rotatable design CCRD for optimization of five variables for Iturin A production and surface tension reduction by B. amyloliquefaciens RHNK22

Run SC IS IP pH TEMP Iturin A production (mg/L) ST (mN/m)

Pred. value Exp. Value Pred. value Exp. Value

1 -1(2%) -1(1%) -1(24h) -1(6.0) 1(370C) 744.2 730.0

2 1(4%) -1(1%) -1(24h) -1(6.0) -1(30°C) 821.2 809.0

3 -1(2%) 1(2%) -1(24h) -1(6.0) -1(300C) 613.5 621.0

4 1(4%) 1(2%) -1(24h) -1(6.0) 1(37°C) 638.1 636.0

5 -1(2%) -1(1%) 1(48h) -1(6.0) -1(30°C) 750.1 746.0

6 1(4%) -1(1%) 1(48h) -1(6.0) 1(37°C) 832.8 819.0

7 -1(2%) 1(2%) 1(48h) -1(6.0) 1(37°C) 533.0 539.0

8 1(4%) 1(2%) 1(48h) -1(6.0) -1(30°C) 604.0 612.0

9 -1(2%) -1(1%) -1(24h) 1(8.0) -1(30°C) 578.9 581.0

10 1(4%) -1(1%) -1(24h) 1(8.0) 1(37°C) 544.5 537.0

11 -1(2%) 1(2%) -1(24h) 1(8.0) 1(37°C) 691.8 704.0

12 1(4%) 1(2%) -1(24h) 1(8.0) -1(30°C) 714.8 729.0

13 -1(2%) -1(1%) 1(48h) 1(8.0) 1(37°C) 513.5 514.0

14 1(4%) -1(1%) 1(48h) 1(8.0) -1(30°C) 608.5 611.0

15 -1(2%) 1(2%) 1(48h) 1(8.0) -1(30°C) 616.7 639.0

16 1(4%) 1(2%) 1(48h) 1(8.0) 1(37°C) 516.4 529.0

17 -2(1%) 0(1.5%) 0(36h) 0(7.0) 0(35°C) 592.8 581.0

18 2(5%) 0(1.5%) 0(36h) 0(7.0) 0(35°C) 652.4 656.0

19 0(3%) -2(0.5%) 0(36h) 0(7.0) 0(35°C) 722.3 750.0

20 0(3%) 2(2.5%) 0(36h) 0(7.0) 0(35°C) 605.9 570.0

21 0(3%) 0(1.5%) -2(12h) 0(7.0) 0(35°C) 685.6 690.0

22 0(3%) 0(1.5%) 2(60h) 0(7.0) 0(35°C) 592.6 580.0

23 0(3%) 0(1.5%) 0(36h) -2(5.0) 0(35°C) 693.1 710.0

24 0(3%) 0(1.5%) 0(36h) 2(9.0) 0(35°C) 505.1 480.0

25 0(3%) 0(1.5%) 0(36h) 0(7.0) -2(25°C) 735.8 720.0

26 0(3%) 0(1.5%) 0(36h) 0(7.0) 2(40°C) 662.4 670.0

27 0(3%) 0(1.5%) 0(36h) 0(7.0) 0(35°C) 643.6 647.0

28 0(3%) 0(1.5%) 0(36h) 0(7.0) 0(35°C) 643.6 641.0

29 0(3%) 0(1.5%) 0(36h) 0(7.0) 0(35°C) 643.6 642.0

30 0(3%) 0(1.5%) 0(36h) 0(7.0) 0(35°C) 643.6 643.0

31 0(3%) 0(1.5%) 0(36h) 0(7.0) 0(35°C) 643.6 648.0

32 0(3%) 0(1.5%) 0(36h) 0(7.0) 0(35°C) 643.6 649.0

33.4 33.3

30.396 30.067 30.441 28.569 27.871 26.835 93 .3 1 7 30.747 27.514 28.273 28.391 33.443 32.063 29.808 29.758 31.603 30.278 30.239 30.353 29.498 30.547 30.911 29.997 31.864 31.423 31.1 31.1 31.1 31.1 31.1 31.1

30.466 30.184 30.689 28.508 27.841 26.192 33.285 33.156 31.176 27.640 28.524 28.689 33.421 32.089 29.959 29.606 31.254 30.441 30.140 30.266 29.165 31.004 31.184 29.537 31.421 31.680 31.034 31.245 31.176 31.284 31.046 31.054

SC- substrate concentration, IS- inoculum size, IP- incubation period, Temp- temperature

Table 9

Coefficients and t-values for iturin A production by B. amyloliquefaciens RHNK22 using central composite rotatable design (CCRD)_

Coefficient Standard error Coefficient t- value Probability

Constant 643.61

Substrate 14.92

IS -29.08

IP -23.25

pH -47.00

Temperature -18.33

Substrate*Substrate -5.24

IS*IS 5.14

IP*IP -1.11

pH*pH -11.11 Temperature*Temperature 13.89

Substrate*IS -12.63

Substrate*IP 3.63

Substrate*pH -17.00 Substrate*Temperature -8.75

IS*IP -25.25

IS*pH 65.88

IS*Temperature -2.88

IP*pH -11.12

IP*Temperature -4.62

pH*Temperature -13.25

8.900 4.554 4.554 4.554 4.554 4.554 4.120 4.120 4.120 4.120 4.120 5.578 5.578 5.578 5.578 5.578 5.578 5.578 5.578 5.578 5.578

72.320 3.275 -6.386 -5.105 -10.320 -4.025 -1.272 1.247 -0.

-2.698

63 0.650 -3.048 -1.569 -4.527 11.810 -0.515 -1.994 -0.829 -2.375

0.000 0.007 0.000 0.000 0.000 0.002 230 38 0.792 0.021 0.006 0.045 0.529 0.011 0.145 0.001 0.000 0.616 0.071 0.425 0.037

S = 22.31; R-Sq = 97.4%; R-Sq (adj) = 92.7%

-Sq (adj)

Table 10

Coefficients and t-values for surface tension reduction by B. amyloliquefaciens RHNK22 using central composite rotatable design (CCRD)

Coefficient Standard error Coefficient t- value Probability

Constant 31.1112

Substrate -0.3313

IS 0.0286

IP 0.9279

pH -0.2285

Temperature -0.1104

Substrate*Substrate -0.0427

IS*IS -0.2038

IP*IP -0.6050

pH*pH -0.1644 Temperature*Temperature 0.1331

Substrate*IS 0.0830

Substrate*IP 0.3340

Substrate*pH -0.2368

Substrate*Temperature -0.9519

IS*IP 0.4091

IS*pH -0.9706

IS*Temperature -0.1232

IP*pH 0.3404

IP*Temperature 0.5020

pH*Temperature -0.1422

0.17288 0.08847 0.08847 0.08847 0.08847 0.08847 0.08003 0.08003 0.08003 0.08003 0.08003 0.10836 0.10836 0.1083 0.10836 0.10836 0.10836 .10836 0.10836 0.10836 0.10836

179.960 0.000

-3.744 0.003

0.323 0.753

10.488 0.000

-2.583 0.025

-1.248 0.238

-2.546 0.605 0.027

-7.560 0.000

-2.054 0.064

1.663 0.124

0.766 0.460

3.082 0.010

-2.185 0.051

-8.785 0.000

3.776 0.003

-8.958 0.000

-1.137 0.280

3.141 0.009

4.633 0.001

-1.313 0.216

S = 0.4334 R-Sq = 97.5% R-Sq (adj)

= 92.8%

Table 11

Analysis of variance for iturin A production by B. amyloliquefaciens RHNK22 using CCRD

Source

DF Seq SS Adj SS Adj MS

Regression 20 205083 205083.3 10254.2 20.60

Linear 5 99697 99696.5 19939.3 40.05

Square 5 11880 11880.3 2376.1 4.77

Interaction 10 93507 93506.5 9350.7 18.78

Residual Error 11 5476 5476.2 497.8

Lack-of-Fit 6 5418 5418.2 903.0 77.85

Pure Error 5 58 58.0 11.6

Total 31 210559

0.000 0.000 0.015 0.000

DF, degrees of freedom; Seq.SS sum of squares; Adj.SS, adjusted sum of adjusted sum of squares; F, Variance ratio; P, Probability.

' squares;

^dj. MS,

Table 12

Analysis of variance for surface tension reduction by B. amyloliquefaciens RHNK22 using CCRD

Source DF Seq SS Adj SS Adj MS F P

Regression 20 79.3313 79.3313 3.96657 21.11 0.000

Linear 5 24.8634 24.8634 4.97269 26.47 0.000

Square 5 12.9744 12.9744 2.59488 13.81 0.000

Interaction 10 41.4935 41.4935 4.14935 22.09 0.000

Residual Error 11 2.0665 2.0665 0.18786

Lack-of-Fit 6 2.0028 2.0028 0.33381 26.23 0.001

Pure Error 5 0.0636 0.0636 0.01273

Total 31 81.3978

DF, degrees of freedom; Seq.SS sum of squares; Adj.SS, adjusted si adjusted sum of squares; F, Variance ratio; P, Probability

squares; Adj. MS,