Application of Plackett–Burman experimental design and Box and Wilson design to improve broad-spectrum antibiotic production by novel Streptomyces sp. 2A

Deepika Sharma and Rajesh Kumari Manhas*

Department of Microbiology, Guru Nanak Dev University, Amritsar 143005, India

*Corresponding author:

Dr. Rajesh Kumari Manhas

Reader,

Department of Microbiology

Guru Nanak Dev University, Amritsar 143005, India

Email: rkmanhas@rediffmail.com

Tel: +91-183-2258802-09 Ext.3450; Fax: +91-183-2258819-20.

Abstract

Isolation of broad spectrum antimicrobial compound producing actinomycete and study of the medium composition for improvement of the antimicrobial production are the goals of the present work. Among a total of one hundred and thirty actinomycetes, isolate 2A showed antimicrobial activity against all the test organisms including drug resistant bacteria. 16S rRNA gene sequence analysis showed that strain 2A belongs to the genus Streptomyces and exhibited highest 16S rRNA gene sequence similarity (99.8 %) with Streptomyces flavotricini NBRC12823T (AB184173). Comparison of physiological characteristics and DNA–DNA hybridization (manifested 36.7% similarity) with S. flavotricini suggests that 2A is a novel strain (GenBank accession no. GQ906975). The traditional one-variable-at-a-time optimization strategy and statistically based experimental designs Plackett-Burman design (PBD) and Box and Wilson design were applied to provide an efficient approach for optimization. Statistical analysis using PBD demonstrated that KNO3, K2HPO4 and NaCl had significant positive influences on antibiotic production. Response surface methodology (RSM) was used for the study of individuals’ interaction and effects of these factors on production. In optimized medium, antimicrobial compound production was increased by 1.5-fold as compared to the basal production medium. In addition, the optimization enhanced the antimicrobial activity spectrum.

Keywords: Novel Streptomyces sp. 2A, broad-spectrum, antimicrobial activity, PBD, RSM

Introduction

Microbial natural products appear as the most promising sources for developing future antibiotics (1) and special attention has been directed towards actinomycetes as they are the major source of novel bioactive metabolites such as herbicides, pesticides, anti-parasitic agents, and enzymes (2). They are the most widely distributed group of microorganisms in nature which primarily inhabit the soil (3). Among actinomycetes, genus Streptomyces has long been recognized as prolific producer of useful secondary metabolites and produces more than half of the known microbial bioactive substances (4-6). They are still a rich source of novel bioactive compounds and many more antibiotics remain to be identified (7).

However, resistance to each new antibiotic has ultimately emerged, usually within a few years after the antibiotic is marketed (8). Among the most resistant bacteria causing important and community acquired infections are methicillin resistant Staphylococcus aureus (MRSA), vancomycin resistant Enterococcus (VRE), vancomycin resistant Staphylococcus aureus (VRSA), Extended Spectrum β-lactamase (ESBL) producing E. coli and Klebsiella spp. and multiple drug resistant Mycobacterium tuberculosis. One-third nosocomial infections occur due to multiple drug resistant Gram-positive bacteria (9). It is imperative to search new, efficient and safe antibiotics to combat the problem of microbial resistance and development of new antimicrobials to substitute the ineffective ones. The current pipeline of antibiotics is primarily focused on MRSA and Gram-positive cocci, although new peptides are in development with promising efficacy against Gram-negative bacteria (10).

In order to obtain optimum yield of an antibiotic, development of a suitable medium and cultural conditions is obligatory. The traditional one-variable-at-a-time optimization strategy is simple and useful for screening independent variables. However, this simple method frequently fails to locate the region of optimum response because the joint effects of factors on the response are not taken into account. Statistically based experimental designs are the powerful design techniques for the media optimization (11-13). The application of these designs in fermentation process results in improved product yields, reduced process variability, closer confirmation of the output response to nominal and target requirements and reduced development, and overall costs (14).

The present study was aimed to design an optimal medium for antibiotic production by 2A, a novel strain of genus Streptomyces, exhibiting strong antimicrobial activity against MRSA and ESBL producing E. coli and P. aeruginosa, using a combination of both classical and statistical approaches.

Materials and Methods

Isolation and maintenance

A total of one hundred thirty actinomycete cultures were isolated and screened for antimicrobial activity. Actinomycete isolate 2A, isolated from the soil sample collected from Guru Nanak Dev University campus, Amritsar (Punjab, India) was chosen to continue the study as it exhibits strong antimicrobial activity against wide range of Gram positive and Gram negative bacteria and yeasts. Physiological and molecular studies suggested that 2A is a novel strain of the genus Streptomyces. The 16S rRNA sequence analysis of 2A (GenBank accession no. GQ906975) showed highest similarity of 99.8% with Streptomyces flavotricini NBRC12823T (AB184173). DNA–DNA relatedness between strain 2A and S. flavotricini NBRC12823T (AB184173) was found to be 36.7 %. The culture is maintained as suspension of spores and mycelia fragments in 20% glycerol (v/v) at -80oC.

Test organisms

Bacillus megaterium (MTCC 428), B. subtilis (MTCC 619), Enterobacter aerogenes (MTCC 111), E. coli (MTCC 1885), K. pneumoniae sub sp. pneumoniae (MTCC 109), Proteus mirabilis (MTCC 1429), Salmonella typhi (MTCC 733), Candida albicans (MTCC 3017), Rhodotorula rubra (MTCC 248), Pseudomonas syringe (MTCC 1604), Mycobacterium smegmatis (MTCC 6) and clinical multi drug resistant bacteria viz. MRSA, Enterococcus, ESBL producing E. coli (R-6SLF, S1-LF) and Pseudomonas aeruginosa (resistant to cefepime and imipenum).

Production

For the development of inoculum, 25 ml of starch casein nitrate medium (g/l: starch, 10; casein, 0.3; KNO3, 2; NaCl, 2; K2HPO4, 2; MgSO4.7H2O, 0.05; CaCO3, 0.02; FeSO4.7H2O, 0.01) having pH 7.0 was dispensed in 100 ml Erlenmeyer. Seed medium was then inoculated with the 1 ml spore suspension (OD 0.8) prepared in sterile water by scraping spores from 7 day old slant and incubated at 28 °C on an orbitek rotary shaker (180 rpm) for 48 h. The experimental medium flasks were inoculated with 2% v/v inoculum and incubated at 28°C at 180 rpm for 96 h. The culture supernatant was obtained by centrifugation at 10,000 rpm for 30 min at 4°C and used for bioassay against test bacteria.

Determination of antibacterial activity by agar well diffusion method

Antimicrobial activity of culture supernatant was determined using agar well diffusion assay (15). The plates containing Mueller-Hinton Agar and Yeast Malt Agar plates were spread with 100 μl of the test organism inoculum (OD equivalent to MacFarland standard 0.5). Wells of 6 mm diameter were cut out from agar plates and filled with 50μl of the culture supernatant. The diameter of the resultant zone of inhibition was measured in mm after 24 h and 48 h for bacteria and yeasts, respectively. Each experiment was performed in duplicates and repeated thrice.

Classical optimization

The effect of various carbon and nitrogen sources on antibiotic production by Streptomyces sp. 2A was investigated with the one variable at a time strategy. The carbon sources: glucose, xylose, fructose, maltose, sucrose, inositol, raffinose, lactose, glycerol, soluble starch and carboxymethyl cellulose were supplemented in the medium at an initial concentration of 1% (w/v). Different nitrogen sources: L-asparagine, soybean meal, yeast extract, NH4NO3, KNO3, casein, ammonium sulphate and peptone at initial concentration of 0.2% (w/v) were tested to determine their impact on production.

Statistical optimization

According to the PDB design (16) all the eight independent variables were organized in 12 combinations (in duplicates). Each variable was examined at a high level (coded +1) and a low level (coded -1). A total of 25 different trails, obtained using Plackett-Burman design matrix by varying the levels of eight culture variables, were examined (Table 1). The experiment was repeated twice. The main effect of each variable was determined using the following equation:

Exi = (∑Mi+ -∑Mi-) / N

where Exi is the variable main effect, Mi+ and Mi- are the response percentage in trails, in which the independent variable (xi) is present in high and low concentrations, respectively and N is the half number of trails. Using Microsoft Excel, statistical t-values for equal unpaired samples were calculated for determination of variable significance.

RSM experiment was carried out according to the Box and Wilson design (17). Three independent variables NaCl (X1), KNO3 (X2) and K2HPO4 (X3) were chosen for study (Table 2). The model was studied within a range of variables designated as low (-1), middle (0) and high (+1) concentration (2). Contour graphs were plotted and regression coefficients were calculated using MINITAB statistical software (MINITAB Inc., version 15.0, PA, USA). The model proposes the following quadratic equation:

G = βo +β1X1 + β2X2 +β3X3 +β11X12+β22X22 +β33X32 +β12 X1X2 +β13X1X3 +β23X2X3

Where G is predicted response; βo is the intercept; β1, β2 and β3, linear coefficients; β11, β22 and β33, the squared coefficients; β12, β13, β14 and β23 the interaction coefficients.

Results and Discussion

Streptomyces sp. 2A exhibited broad-spectrum antimicrobial activity against all the test organisms; inhibition zone in mm is shown in fig. 1.

Classical optimization

The effect of various carbon sources on antibiotic production (after 96 h of incubation) by Streptomyces sp. 2A was studied and starch was found to be the best carbon source for antibiotic production and growth followed by glucose and fructose (fig. 2). Starch was also proved to be the best source for gentamicin production by Micromonospora echinospora (18). The effect of nitrogen sources on antibiotic production was examined in starch casein medium containing different inorganic and organic nitrogen sources. Among the various nitrogen sources, the highest activity and growth was obtained with KNO3 and comparable activity with ammonium sulfate and ammonium nitrate (fig. 3). Streptomyces kananmyceticus M27 yielded the maximum antibiotic production with ammonium sulfate (19).

Statistical optimization

The design plan and the averages of antimicrobial activity (response) for the different trials are recorded in terms of inhibition zone (mm). The highest activity against all test bacteria was recorded in trial no. duplicate 20 (23.0, 12.0 and 16.0 mm against B. subtilis, K. pneumoniae and MRSA, respectively) while lowest was in trial no. 12 (16.0 mm against B. subtilis and no activity against K. pneumoniae and MRSA). The statistical analysis (t-values) demonstrated that KNO3, K2HPO4, NaCl and starch had significant positive influences on production with main effects ranging from 1.0 to 6.6667 (Table 1). The data obtained showed a range of positive main effect values indicating that the presence of high levels of KNO3, K2HPO4, NaCl and starch in the medium positively effects the production while casein, MgSO4.7H2O, FeSO4.7H2O and CaCO3 have negative effects on the production. Based on the results obtained from PBD, a medium containing (g/l): starch, 5; casein, 0.1; KNO3, 3.0; K2HPO4, 3.0; MgSO4.7H2O, 0.1; FeSO4.7H2O, 0.01; CaCO3, 0.03; NaCl, 3.0 was predicted to be near the optimum for production. In order to evaluate the accuracy of the Plackett-Burman test, verification experiment was undertaken, in which the predicted optimum levels of the independent variables were investigated and compared to the basal medium.

The objective of the RSM study was to find the optimal combination of KNO3, K2HPO4, and NaCl for enhanced antibiotic production. The experiments were carried out according to the experimental plan (consisting of 17 different trials) and results were analyzed using Minitab 15.0 software. The t-test and p-values were used to identify the effect of each factor on antimicrobial production (Table 2).The larger the magnitude of the t-value and smaller the P-value, the more significant the corresponding coefficient. A P-value of less than 0.05 indicates that the model terms are significant and regression models having a coefficient of determination R2 value higher than 0.9 was considered as having a very high correlation (20). The R2 values were found to be 95.2, 92.9 and 92.8% for B. subtilis, K. pneumoniae and MRSA, respectively. Therefore, the present R2-value reflected a very good fit between the observed and predicted responses, and implied that the model is suitable for antibiotic production by Streptomyces 2A. The model can be shown as follows:

G (B. subtilis) = 23.8 +2.50X1 + 4.0X2 + 1.00X3 +1.0X12 -4.40X22- 0.90X32 -2.0 X1X2 -0.5X1X3 +0.5X2X3

G (K. pneumoniae) = -9.22 +2.230X1 +19.899 X2 -0.854X3 -0.041X12 -4.368X22 - 0.178X32 -0.185 X1X2 +0.549X1X3 +0.148X2X3

G (MRSA) = 15.8 +2.625X1 +4.375 X2 +2.000X3 -2.275X12 -5.275X22 - 1.025X32 +0.250 X1X2 +0.500X1X3 -2.000X2X3

Where G is the predicted antibiotic yield, X1, X2 and X3 are the uncoded values of the NaCl, KNO3 and K2HPO4 respectively. The quadratic main effects and first-order effect of KNO3 are significant for all the three test organisms. The quadratic main effects of NaCl are significant for B. subtilis and MRSA. The interactions of NaCl and KNO3 for B. subtilis are significant (p< 0.028) however the other interactions are insignificant (p> 0.05). An analysis of variance (ANOVA) for the response surface square model suggests that the concentrations of NaCl and KNO3 have direct effect on antimicrobial activity and hence yield (Table 3). The model also showed statistically insignificant lack of fit (P) 0.512, 0.004 and 0.014 in case of B. subtilis, K. pneumoniae and MRSA, respectively, so the model was supposed to be adequate for prediction within the range of variables employed.

In order to gain a better understanding of the effect of concentration of three independent variables: NaCl, K2HPO4 and KNO3 on the antibiotic production the three-dimensional response surface plots and contour plots for B. subtilis, K. pneumoniae and MRSA were plotted (Fig. 4). The coordinates of the central point correspond to optimal of the each variable. Response surface plots as a function of two variables at a time maintaining the third variable at a fixed level (zero, coded level) were helpful in understanding both main and interaction effects of these variables. All of the above considerations indicated an excellent adequacy of the regression model. From response surface contour plots, the optimal values of the variables for B. subtilis X1 = 3.0, X2 =2.5 and X3 =1.65 (g/L); for K. pneumoniae X1 = 3.0, X2 = 2.0 and X3 =1.65 (g/L); and for MRSA X1 = 2.5, X2 = 2.3 and X3 =1.65 (g/L) were obtained.

Further validation experiments were also carried out to verify the adequacy and the accuracy of the model, and results showed that the predicted value agreed with the experimental values well. PBD and RSM results revealed that NaCl, K2HPO4 and KNO3 are important and very significant factors in antibiotic synthesis by novel Streptomyces sp. 2A. Enhanced yield of antibiotic against test organisms was obtained with the optimized production medium, showing inhibition zones of 28 mm, 13mm and 17mm against B. subtilis K. pneumoniae and MRSA, respectively. In optimized medium, antimicrobial compound production was increased by 1.5-fold as compared to the basal production medium. The statistical optimization increased 1.45-fold avermectin production B1a by Streptomyces avermitilis 14-12A (21). Streptomyces sp. 2A does not exhibited antimicrobial activity against Pseudomonas aeruginosa (resistant to cefepime and imipenum) in the basal medium but exhibited activity (14 mm inhibition zone) in medium optimized using RSM. This study proved that statistical experimental designs offer an efficient and feasible approach for optimization of production media.

Acknowledgment

The authors are thankful to Department of Science and Technology, New Delhi, India for providing the financial assistance.

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Table 1 The Plackett–Burman design for screening variables in antibiotic production

Factors (g/l)	Code	Low level (-1)	0	High level (+1)	Effect	Coef	St Dev Coef	t-values	p-values
Bs	Kp	Ms	Bs	Kp	Ms	Bs	Kp	Ms	Bs	Kp	Ms	Bs	Kp	Ms
Constant	18.417	4.458	9.0833	0.3058	0.7781	0.5835	60.22	5.73	15.5	0.000	0.000	0.000
Starch	X1	5.0	10.0	15.0	1.667	0.583	1.6667	0.833	0.292	0.8333	0.3058	0.7781	0.5835	2.73	0.37	1.42	0.016	0.713	0.175
Casein	X2	0.1	0.3	0.5	0.500	-2.250	-0.000	0.250	-1.125	-0.000	0.3058	0.7781	0.5835	0.82	-1.45	-0.00	0.426	0.169	1.000
KNO3	X3	0.5	2.0	3.0	3.167	3.250	3.8333	1.583	1.625	1.9167	0.3058	0.7781	0.5835	5.18	2.09	3.27	0.000	0.054	0.005
K2PHO4	X4	0.3	2.0	3.0	2.167	4.250	5.3333	1.583	2.125	2.6667	0.3058	0.7781	0.5835	3.54	2.73	4.56	0.003	0.015	0.000
MgSO4.7H2O	X5	0.03	0.05	0.1	0.167	-0.583	-1.500	0.083	-0.295	-0.750	0.3058	0.7781	0.5835	0.27	-0.37	-1.28	0.789	0.713	0.220
FeSO4.5H2O	X6	0.01	0.01	0.03	-2.00	-0.75	-2.833	-1.000	-0.375	1.4167	0.3058	0.7781	0.5835	-3.27	-0.48	2.42	0.005	0.637	0.029
CaCO3	X7	0.01	0.02	0.03	0.500	0.417	1.6667	0.250	-0.208	0.8333	0.3058	0.7781	0.5835	0.82	-0.27	1.42	0.426	0.739	0.175
NaCl	X8	0.3	2.0	3.0	1.000	5.250	6.6667	0.500	2.625	3.3333	0.3058	0.7781	0.5835	1.64	3.37	5.56	0.123	0.004	0.000

Bs=B. subtilis, Kp= K. pneumoniae, Ms = MRSA, 0= Original medium

Table 2 Regression coefficients of the variables affecting antibiotic production.

Term	Coefficient	St.Dev.Coef	T	P
Bs	Kp	Ms	Bs	Kp	Ms	Bs	Kp	Ms	Bs	Kp	Ms
Constant	23.800	-9.220	15.800	0.6503	3.6638	0.9569	36.000	-2.516	16.511	0.000	0.040	0.000
X1	2.500	2.230	2.625	0.5141	2.2130	0.7565	4.863	1.008	3.470	0.000	0.347	0.010
X2	4.000	19.899	4.375	0.5141	2.5468	0.7565	7.781	7.813	5.783	0.000	0.000	0.000
X3	1.000	-0.854	2.000	0.5141	2.2130	0.7565	1.945	-0.386	2.644	0.093	0.711	0.033
X1* X1	1.100	-0.041	-2.275	0.7086	0.5149	1.0428	1.552	-0.080	-2.182	0.165	0.939	0.065
X2* X2	-4.400	-4.368	-5.275	0.7086	0.6005	1.0428	-6.209	-7.274	-5.059	0.000	0.000	0.000
X3* X3	-0.900	-0.178	-1.025	0.7086	0.5149	1.0428	-1.270	-0.346	-0.983	0.245	0.739	0.358
X1* X2	-2.000	-0.185	0.250	0.7270	0.5702	1.0699	-2.751	-2.077	0.234	0.028	0.076	0.822
X1* X3	0.500	0.549	0.500	0.7270	0.5282	1.0699	0.688	1.039	0.467	0.514	0.333	0.654
X2* X3	0.500	0.148	-2.000	0.7270	0.5705	1.0699	0.688	0.260	-1.869	0.514	0.803	0.104

B. subtilis (Bs) S= 1.454; R-Sq = 95.2%; R-Sq (adjust) = 89.0%.

K. pneumoniae (Kp) S= 1.925; R-Sq = 92.9%; R-Sq (adjust) = 87.37%.

Methicillin resistant Staphylococcus aureus (Ms) S= 2.140; R-Sq = 92.8%; R-Sq (adjust) = 83.5%.

Table 3 ANOVA of regression model.

Source	DFa	Seq. SSb	Adj. SS Bs	Adj. MSc	F	P
Bs	Kp	Ms	Bs	Kp	Ms	Bs	Kp	Ms	Bs	Kp	Ms	Bs	Kp	Ms
Regression	9	293.671	338.05	411.009	293.671	338.05	411.009	32.630	37.5611	45.6676	15.43	10.13	9.97	0.001	0.003	0.003
Linear	3	186.000	118.75	240.250	186.000	118.75	240.250	62.000	78.0206	80.0833	29.32	21.05	17.49	0.000	0.001	0.001
Square	3	89.671	199.05	153.509	89.671	199.05	153.509	29.072	66.3500	51.1696	14.14	17.90	11.18	0.002	0.001	0.005
Interactions	3	18.000	20.250	17.250	18.000	20.250	17.250	6.000	6.7500	5.7500	2.84	1.82	1.26	0.116	0.231	0.360
Residual error	7	14.800	25.950	32.050	14.800	25.950	32.050	2.114	3.7071	4.5786
Lack of fit	3	6.000	24.750	29.250	6.000	24.750	29.250	2.000	8.2500	9.7500	0.91	27.50	13.93	0.512	0.004	0.014
Pure error	4	8.800	1.200	2.800	8.800	1.200	2.800	2.200	0.3000	0.7000
Total	16	364.00	443.059

a DF, Degree of freedom;b SS, sum of squares;

c MS, mean square.

B. subtilis (Bs)

K. pneumoniae (Kp)

Methicillin resistant Staphylococcus aureus (Ms)

Figure 1. Antimicrobial activity of Streptomyces 2A against various test organisms

Figure 2 Effect of carbon sources on antibiotic production

Figure 3 Effect of nitrogen sources on antibiotic production

(a) (b)

(c)

Figure 4 Response surface/contour plot showing effect of independent variables NaCl, K2HPO4 and KNO3 concentration on antibiotic production against (a) B. subtilis (b) K. pneumoniae and (c) MRSA