Enzyme and Microbial Technology 37 (2005) 704–711

Medium optimization for the production of the secondary metabolitesqualestatin S1 by aPhoma sp. combining orthogonal design and

response surface methodology

Roberto Parra, David Aldred, Naresh Magan∗

Applied Mycology Group, Institute of BioScience and Technology, Cranfield University, Silsoe,Bedford MK45 4DT, UK

Received 25 October 2004; accepted 11 April 2005

Abstract

In the present work, a combined statistical approach of orthogonal design (L27(313)), response surface techniques and polynomial regressionwere applied to optimize the composition and concentration of a liquid fermentation medium for the production of squalestatin S1 by a fungus(a Phoma species). Optimal conditions for maximal titres and productivity were determined based on 13 parameters at three different levels.I e responses precursorsw nditions forof ty whenc tegration ofk ogrammes.©

K

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tStbcptttif

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e asenddarys, ni-of en-nd in-l by

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owthuallyal ton-

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nitially, a screening design methodology was used to evaluate the process variables, which were relevant to S1 titre and thurfaces applied to find optimal regions for production. The sources of carbon and concentration, and their interactions with oilyere statistically significant factors. The combined orthogonal design and response surface methodology predicted optimal cof 273 mg l−1 of squalestatin S1. Confirmatory experiments of the optimal medium composition produced titres of 434 mg l−1 in a 5-day

ermentation at 25◦C. This represented a 60% improvement in the maximum titre predicted, and a two-fold higher productiviompared with reported S1 yields of various fungal species. This combined statistical approach enables rapid identification and iney medium parameters for optimising secondary metabolite production and could be very useful in pharmaceutical screening pr2005 Elsevier Inc. All rights reserved.

eywords: Medium optimization; Orthogonal design; Response surfaces; Squalestatin S1;Phoma sp

. Introduction

The treatment of hypercholesterolemia with pharmaceu-ical agents reduces the risk of developing arteriosclerosis.everal therapies such as bile acid sequestrants or choles-

erol biosynthetic inhibitors are available. In the isoprenoidiosynthetic pathway, the first step to the biosynthesis ofholesterol involves the dimerization of farnesyl pyrophos-hate to squalene. This step is catalyzed by squalene syn-

hase and is a potential drug target. Substrates analogous ofhe farnesyl pyrophosphate have been synthesized and foundo be inhibitors of this enzyme. Squalestatins are a potentnhibitor of the squalene synthase[1]. Among the variousungal species that produce squalestatins (= zaragozic acids)

∗ Corresponding author. Tel.: +44 1525 863539; fax: +44 1525 863540.E-mail address: n.magan@cranfield.ac.uk (N. Magan).

as secondary metabolites, aPhoma sp. is particularly important for achieving high yield capacity[2,3].

Some key intermediates of primary metabolism servbranching points of biosynthetic pathways leading toproducts of primary and secondary metabolism. Seconmetabolism is regulated by precursors, carbon sourcetrogen sources, phosphate, trace elements, inductionzymes of secondary metabolism, catabolic repression ahibition, feed back repression and inhibition and controauto-regulators[4].

The available precursors inside cells may regulateondary metabolite production, especially when the spesynthase enzymes are already active in the cells[5]. Theremay be differences between the carbon sources for grand secondary metabolism. For example, glucose is usan excellent source for growth, but may not be beneficisecondary metabolism[6]. The synthesis of pectinolytic e

141-0229/$ – see front matter © 2005 Elsevier Inc. All rights reserved.oi:10.1016/j.enzmictec.2005.04.009

R. Parra et al. / Enzyme and Microbial Technology 37 (2005) 704–711 705

zymes is considerably influenced by glucose concentrationof the cultivation medium requiring a predetermined optimalratio of mixed carbon sources[7].

The effect of nitrogen sources on secondary metabolism isconditioned by several factors including the type of metabolicpathway, the producing organism, the type and concentrationof the nitrogen sources and whether cultures are stationaryor submerged. Very often, secondary metabolic pathwaysare negatively affected by nitrogen sources favourable forgrowth[4]. Negative effects of ammonium salts have been re-ported in the production of cephalosporin and other metabo-lites/antibiotics[6].

It is known that several trace elements are essential formicrobial growth because of their involvement in metalloen-zymes or as enzyme activators. In secondary metabolism,zinc, iron and manganese are the most important trace el-ements. Several reports have been published on the impor-tance of these three elements in aflatoxin production[4]. Ithas been shown that zinc is an essential element for aflatoxinbiosynthesis. The omission of zinc resulted in no detectableversicolorin production by a blocked mutant ofAspergillusparasiticus [4]. Inorganic phosphate is also known to ex-ert a suppressive effect on the synthesis of many secondarymetabolites.

In developing a biotechnology-based industrial process,designing the fermentation media is of critical importance.T olu-m t oft over-a veryt ds iden-t ingp

n ap-p to ana sed norei inge e in-f omei hasb ulti-p delo atat res-s sed toi rod-u ingc opti-m hee uiredf ions

tes,i rent

levels in submerged fermentation, to produce squalestatin S1by a Phoma species. This approach used a combination ofstatistical strategies involving the use of an orthogonal de-sign, response surfaces and polynomial regression to find thebest medium for maximising yields of S1 in a submergedfermentation system.

2. Materials and methods

2.1. Fungal strain

Stock spore suspension of thePhoma sp. (IMI 332962)was maintained in 0.5-ml cryogenic vials (Nalgene). Theculture was maintained as aliquots of the spore suspension.(1.6 E + 007 spores/ml± 3%) with Tween 80 (100�l l−1)and 5% (w/v) glycerol stored at−70◦C. The culture andS1 standard were kindly supplied by GlaxoSmithKlineR&D, Medicines Research Centre, Stevenage, Herts SG12NY, UK.

2.2. Activation and strain inoculation

Thawed 500�l cryogenic stock spore suspension vials(Nalgene) ofPhoma sp. were used. A 5-�l spore suspen-sion was inoculated on glycerol-modified MEA (0.98a)P -Ii en8 ong tiv-i log-i ionw dingt icalflf

2

tiono newo sucha ment( ents(

oft(((

2

cing2 ids

he fermentation medium affects the product yield and vetric productivity. It is also important to reduce the cos

he medium as much as possible, as this may affect thell process economics. Medium screening studies are

ime consuming and expensive[8]. For economy of effort ancale, different approaches have been used to rapidlyify the variables, which need to be controlled for optimisroduction of useful metabolites.

Orthogonal and Plackett–Burman designs have beelied to try and reduce the number of fermentation runsbsolute minimum[9–11]. The main disadvantage of theesigns is that they consider only first order effects and ig

nteractions. However, while a full factorial design (testvery combination possible) provides the most completormation, it often requires so many runs that they becmpractical to carry out. Thus, optimum performanceeen determined using mathematical tools such as mle regression of a partial or full factorial to obtain a mof the production system, usually involving fitting of d

o a polynomial equation, using stepwise multiple region. Response surface methodology has also been u

nvestigate the optimal regions of production of useful pct [12,13]. Detailed analyses of the optimised region usetroidal or simple designs have also been applied forisation processes[13]. However, several interactions of t

xperimental design and optimisation of models are reqor effective application to product formation in fermentatystems.

The objective of this study was to optimize 13 substrancluding those reported in the literature, at three diffe

wetri plates and incubated at 25◦C [2]. A Novasina Humidat

C-II (Switzerland) was used to check the aw level. Sporenoculum was obtained pouring 10 ml of sterile Twe0 (100�l l−1) solution onto growing cultures producedlycerol-modified malt extract agar (MEA; 0.98 water ac

ty). The spores were dislodged using a sterile microbiocal loop. Two milliliters of the extracted spore suspensere used to inoculate the 100 ml liquid medium accor

o the experimental design. The cultures (in 250-ml conasks) were incubated at 25◦C on a shaker at 150 rev min−1

or 10 days.

.3. Media design and composition

The medium composition was based on a combinaf substrates: some reported in the literature and somenes. The medium design included simple substratess a nitrogen source due to the importance of this eleC:N ratio) in secondary metabolism and also trace elemMg2+ and Fe3+) (Table 1).

The trace element solution (100× strength) consistedhe following components: MnSO4 (1.0 g l−1), CuCl2·2H2O0.025 g l−1), CaCl2·2H2O (0.1 g l−1), H3B03 (0.056 g l−1),NH4)6MoO24·24H2O (0.019 g l−1) and ZnSO4·7H2O0.2 g l−1). The pH was adjusted to 7.

.4. Sample preparation and analyses for squalistatin S1

Preparation of the samples was carried out by pla50�l of the samples in 1 ml Eppendorf tubes with l

706 R. Parra et al. / Enzyme and Microbial Technology 37 (2005) 704–711

Table 1Experimental design combinations for the factors and levels used for theoptimization of squalestatin production based on substrate and concentrationreported

Key Substrate Levels

Low Medium High1a 2a 3a

−1b 0b 1b

SC 1 Glycerol[5,7,8,10] 30 g l−1 50 g l−1 70 g l−1

SC 2 Glucose[3,5,11] 5 g l−1 10 g l−1 15 g l−1

SC 3 GlucoseReplicate 5 g l−1 10 g l−1 15 g l−1

OP 1 Soybean oil[5,8,10] 25 ml l−1 30 ml l−1 35 ml l−1

OP 2 Cottonseed oil 5 ml l−1 10 ml l−1 15 ml l−1

OP 3 Cottonseed flour[5,8] 5 g l−1 10 g l−1 15 g l−1

P 1 Yeast extract[3,7,11] 5 g l−1 7.5 g l−1 10 g l−1

P 2 Soya milk 25 ml l−1 75 ml l−1 125 ml l−1

P 3 Malt extract[1,3,8,11] 15 g l−1 21 g l−1 27 g l−1

TE Trace elements[10] 5 ml l−1 10 ml l−1 15 ml l−1

NaCit Sodium citrate[5,7,10] 8 g l−1 11 g l−1 14 g l−1

N (NH4)SO4 1 g l−1 2 g l−1 3 g l−1

Lac Lactose[7] 35 g l−1 50 g l−1 65 g l−1

Pep Peptone[1,3,8] 5 g l−1 10 g l−1 15 g l−1

Mg MgSO4 [3,8,10,11] 0.01 g l−1 0.5 g l−1 0.9 g l−1

Fe FeSO4·7H2O [10,11] 0.5 g l−1 1.0 g l−1 1.5 g l−1

Aldred et al.[23]; Bergstrom et al.[14]; Blows et al.[1]; Connors et al.[19];Dawson et al.[3]; Dufresne et al.[20,21].

a Design: orthogonal design.b Response surfaces.

(Sartorius) adding 750�l of extractive solvent. This wasmixed for 5 min in a shaker at 250 rpm (L.H. Engineer-ing Co. Ltd., England) and centrifuged in a Microcentaur(MSE) for 15 min at 13,000 rpm. The supernatant was fil-tered through a 0.2�m nylon syringe filter (BDH) and trans-ferred to 2 ml HPLC vials (Anachem) and sealed with septa(0.010′′ PTFE, Anachem) and lids (Anachem). The sampleswere kept at−80◦C and thawed before analysis. The reagentsused in HPLC analysis were: extraction solvent 80% methyl-acetonitrile (MeCN) containing H2SO4 (0.3 ml l−1) and mo-bile phase 50% MeCN containing H2SO4 (0.15 ml l−1). Astock solution of 120�m ml−1 S1 was prepared as a stan-dard and kept frozen at−80◦C. Aliquots were thawed anddiluted in the range of 0.012–120�m ml−1 and placed in2 ml HPLC vials. Dilutions of the samples were made ifnecessary.

A Gilson 715 HPLC system was used to quantifysqualestatin (S1) from the fermentation broth. Aliquots of50�l were automatically injected with a Gilson 231XL sam-pling injector onto a 5�l C6 Spherisob 150 mm× 4.6 mmcolumn (Phenomenex), with a guard column of the samematerial. Separated metabolites were detected with a Gilson117 UV detector at 210 nm. Running conditions of the anal-ysis were: 1 ml min−1, time 30 min per sample. The yieldof S1 in the samples was analyzed by comparison withan internal standard against the samples, and S1 standardw ten-t een8

2.5. Experimental design and data treatment

To investigate the relationship between substrate mediumcomponents and their concentration to optimize the produc-tion of squalestatin S1 by thePhoma species an orthogonaldesign L27 (313) was used.

The medium composition was selected according to theexperimental design. The combinations of substrate and con-centrations were selected using the design module of Statis-tica 6.0 (SoftStat Inc., 1984–2001, USA).

The substrate levels were allocated into three categories:(1) low; (2) medium; and (3) high. An additional dummyvariable of glucose was included in the experimental designto measure the variability of the experimental design. It gavea direct estimate of the standard error of the effect of differentfactors.

For the analysis, the factors were given coded values ofbetween 1 (lowest substrate cncentration) and 3 (highest sub-strate concentration). For the response surface methodol-ogy and the polynomial regression, the coded values werechanged as follows:−1 = 1; 0 = 2; and 1 = 3 and the analysisperformed with the experimental design module of Statistica6.0 (SoftStat Inc., 1984–2001, USA).

3. Results

3

fromt ndi-t ource,h erol.T lI

her l de-s ncedu s int , oilyp hemf

-t theirc ctorsc ses).T gen( rs( re-c velso ate( ar-b tose( andl rtant

ere included randomly to detect changes in the reion time of S1. The retention time of S1 was betw–9 min.

s

.1. Effect of medium constituents on the S1 production

Table 2summarises the mean S1 mean titres obtainedhe experiment in relation to the different treatment coions. When glucose was used as an additional carbon sigher S1 titre was obtained when compared to glycitres of S1 with added glucose ranged from 0 to 273 mg−1.

nterestingly, cottonseed flour gave the highest S1 titre.Table 3shows the analysis of variance (ANOVA) of t

esults after the 5-day fermentation with an orthogonaign analysis. This indicated that S1 titre could be enhasing a combination of solutes at different concentration

he fermentation medium. The sources of carbon (LSC)recursors (OP), nitrogen (N) and peptone (Pep) were tost significant factors in the production of S1 (p < 0.001)

ollowed by precursors (P) and trace elements (TE) (p < 0.05).Using the orthogonal design L27(313) approach, the rela

ionships between medium component variables andoncentrations on S1 titres could be calculated. The faould be ranked in importance (magnitude in parenthehey were: carbon sources (LSC = 43.621) > nitro

N = 41.782) > precursors (P = 38.663) > levels of precursoLOP = 31.455) > trace elements (TE = 29.289) > oily pursor (OP = 28.526) > peptone (Pep = 25.271) > lef precursors (LP = 23.393) > sodium citrNaCit = 18.949) > iron (Fe = 18.768) > sources of con (SC = 15.934) > magnesium (Mg = 2.702) > lacLac = 0.948). This pointed to the sources of carbonevels, nitrogen and precursors being the most impo

R. Parra et al. / Enzyme and Microbial Technology 37 (2005) 704–711 707

Table 2Experimental conditions and mean titres of S1 (+S.E., standard error) produced with the orthogonal design L27(313)

Design id no. SC LSC OP LOP P LP TE NaCit N Lac Pep Mg Fe Squalestatin S1 (mg l−1)

Experimental S.E.

1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 17.1 0.02 −1 −1 −1 −1 0 0 0 0 0 0 0 0 0 153.2 0.03 −1 −1 −1 −1 1 1 1 1 1 1 1 1 1 34.7 0.04 −1 0 0 0 −1 −1 −1 0 0 0 1 1 1 1.7 0.05 −1 0 0 0 0 0 0 1 1 1 −1 −1 −1 7.4 0.06 −1 0 0 0 1 1 1 −1 −1 −1 0 0 0 0.0 0.07 −1 1 1 1 −1 −1 −1 1 1 1 0 0 0 0.0 0.48 −1 1 1 1 0 0 0 −1 −1 −1 1 1 1 26.7 0.69 −1 1 1 1 1 1 1 0 0 0 −1 −1 −1 89.7 0.7

10 0 −1 0 1 −1 0 1 −1 0 1 −1 0 1 273.4 1.411 0 −1 0 1 0 1 −1 0 1 −1 0 1 −1 118.4 2.012 0 −1 0 1 1 −1 0 1 −1 0 1 −1 0 1.6 2.113 0 0 1 −1 −1 0 1 0 1 −1 1 −1 0 128.1 2.414 0 0 1 −1 0 1 −1 1 −1 0 −1 0 1 55.1 2.615 0 0 1 −1 1 −1 0 −1 0 1 0 1 −1 162.9 2.916 0 1 −1 0 −1 0 1 1 −1 0 0 1 −1 15.7 3.017 0 1 −1 0 0 1 −1 −1 0 1 1 −1 0 0.7 3.518 0 1 −1 0 1 −1 0 0 1 −1 −1 0 1 0.0 3.719 1 −1 1 0 −1 1 0 −1 1 0 −1 1 0 253.0 4.720 1 −1 1 0 0 −1 1 0 −1 1 0 −1 1 118.3 5.421 1 −1 1 0 1 0 −1 1 0 −1 1 0 −1 35.5 5.422 1 0 −1 1 −1 1 0 0 −1 1 1 0 −1 0.0 5.723 1 0 −1 1 0 −1 1 1 0 −1 −1 1 0 0.0 6.224 1 0 −1 1 1 0 −1 −1 1 0 0 −1 1 0.0 6.425 1 1 0 −1 −1 1 0 1 0 −1 0 −1 1 271.5 8.026 1 1 0 −1 0 −1 1 −1 1 0 1 0 −1 49.3 8.227 1 1 0 −1 1 0 −1 0 −1 1 −1 1 0 23.7 8.3

For key to factors, seeTable 1.

factors in determining S1 titres. Lactose was the leastimportant nutrient.

Table 4presents the optimal combination and concentra-tion of substrates required to achieve the highest S1 titre. Asearch was carried out to identify the optimum componentsfor maximising titre. The expected optimum S1 titre that this

Table 3The variance analysis of L27(313) orthogonal test on optimization of culturemedium in shake flask culture

SS df MS F p-value

SC 26542.6 2 13271.3 4.592 0.019LSC 53033.59 2 26516.79 9.174 0.001**

OP 52674.18 2 26337.09 9.112 0.001**

LOP 27383.44 2 13691.72 4.737 0.017P 44001.77 2 22000.88 7.612 0.002∗LP 25657.82 2 12828.91 4.439 0.022TE 46360.23 2 23180.12 8.020 0.002∗NaCit 14670.61 2 7335.303 2.538 0.098N 59431.78 2 29715.89 10.281 0.000**

Lac 77.0779 2 38.53895 0.013 0.987Pep 38852.08 2 19426.04 6.721 0.004**

Mg 706.447 2 353.2235 0.122 0.885Fe 9968.942 2 4984.471 1.725 0.197

Residual 78038.03 27 2890.298

For key to treatments, seeTable 1. Key: SS, sum of squares; df, degrees offreedom; MS, mean square.

* p < 0.005.

optimization could produce was 387 mg l−1. This was a 42%increase in titre when compared with the highest concentra-tion (273 mg l−1) achieved in the experiment.

Fig. 1presents a graphical analysis based on the effect ofthe mean squalestatin S1 titre under the sets of conditions andtreatment levels tested in the present study. This describes themost important factors which determine S1 production andshows the impact of changing treatment levels on production.

Table 4Optimal medium design for the production squalestatin S1 and forecastedproduction

Terms Substrates Levels

SC GlucoseLSC 1 5 g l−1

OP Cottonseed flourLOP 1 5 g l−1

P Yeast extractLP 3 10 g l−1

TE Trace elements 10 ml l−1

NaCit Sodium citrate 8 g l−1

N (NH4)SO4 2 g l−1

Lac Lactose 65 g l−1

Pep Peptone 10 g l−1

Mg MgSO4 0.9 g l−1

Fe FeSO4·7H2O 0.5 g l−1

Expected Squalestatins S1 387 mg l−1

S

** p < 0.001. eeTable 1for key to treatments.

708 R. Parra et al. / Enzyme and Microbial Technology 37 (2005) 704–711

Fig. 1. Graphical analysis of the relationship between media formulation and squalestatin S1 titres. Each factor had three coded levels and the (©) representmean squalestatin S1 produced. The factors levels coded are: low (1); medium (2); and high (3) as presented inTable 1.

In order to find the optimum and statistically signifi-cant interactions between factors, they were re-coded to per-form response surface methodology. A first order polyno-mial model with interactions was selected. This confirmedthe robustness and reproducibility of the experiment (data notshown).

The analysis of the model produced anR2 of over 99% andoverall model significance ofp < 0.0001. The model from theanalysis consisted of an intercept, 13 main factor terms, 11 in-teraction terms and interception terms, thus including a totalof 25 terms. The effect of factors, standard error,p-values and95% confidence levels of the parameter variability (parametercertainty) are presented inTable 5. The effects are ranked byp-values from the most to the least significant. In the polyno-mial analysis, S1 yield was shown to be affected principallyby the interaction between level of carbon sources, oily pre-cursor levels and type (p < 0.000001). The yield of S1 hada high correlation coefficient (R2 = 0.99) with the predictedvalues (seeTable 2).

Analyses of the observed versus predicted yields areshown inFig. 2. Points above or below the diagonal line rep-resent areas of over or under prediction. This shows that nosignificant violations of the model were found in the analysis,with a good correlation of the model with the experimentaldata obtained.

Illustrations of the critical effects and interactions be-tween factors were identified by producing contour graphplots. Such contour diagrams represent the production ofS1 (mg l−1) as a function of substrate type or concentra-tion of two nutrients, with the other factors kept at con-stant levels. This shows the reproducibility and robust-

Fig. 2. Residual diagnostics of contour surface of the quadratic model of thepredicted vs. observed by replicated of squalestatin S1 titre (mg l−1).

R. Parra et al. / Enzyme and Microbial Technology 37 (2005) 704–711 709

Table 5Estimated linear and interaction effects of the model ranked by magnitude from the orthogonal experiment statistical analysis

Term Effect S.E. t(18) p-value −95% +95%

Mean/interaction 93.01 6.37 14.61 0.000000 79.6 106.4SC× OP 477.47 44.01 10.85 0.000000 385.0 569.9LOP 308.25 32.28 9.55 0.000000 240.4 376.1LSC 249.31 30.36 8.21 0.000000 185.5 313.1OP −138.73 17.90 −7.75 0.000000 −176.3 −101.1P × LP −368.52 50.63 −7.28 0.000001 −474.8 −262.2NaCit 447.60 64.06 6.99 0.000002 313.0 582.2LSC× P 344.79 50.06 6.89 0.000002 239.6 450.0LSC× NaCit 506.83 76.96 6.59 0.000003 345.1 668.5SC× P 409.84 62.52 6.56 0.000004 278.4 541.2SC× N 393.82 61.60 6.39 0.000005 264.4 523.2LOP 212.98 35.84 5.94 0.000013 137.6 288.3SC× LP −385.98 66.92 −5.77 0.000018 −526.6 −245.4LSC× OP 120.08 23.26 5.16 0.000065 71.2 168.9Pep 272.40 56.23 4.84 0.000130 154.2 390.5Lac 154.38 33.42 4.62 0.000213 84.2 224.6TE 142.94 41.87 3.41 0.003094 54.9 230.9SC× Mg −197.42 59.66 −3.31 0.003904 −322.7 −72.1SC× Pep −105.92 37.45 −2.83 0.011140 −184.6 −27.2N −36.35 15.22 −2.39 0.028126 −68.3 −4.4Fe −139.77 59.77 −2.34 0.031102 −265.3 −14.2SC −65.70 28.42 −2.31 0.032838 −125.4 −6.0Mg −27.15 17.43 −1.56 0.136665 −63.8 9.5OP −21.33 29.39 −0.73 0.477380 −83.1 40.4SC× LSC 12.02 39.67 0.30 0.765333 −71.3 95.4

For key to treatments, seeTable 1.

ness of the polynomial model based on the experimentaldata.

To determine the most adequate operating conditionsand analyze the process for yield, the contour diagramswere plotted using the polynomial equation for all the com-binations possible. Over all conditions, glucose was thebest carbon used (seeFig. 3). The optimum oily precur-sor (OP) was cottonseed flour (1) which gave yields of180–190 mg l−1 of S1 (Fig. 3a). For nitrogen addition, anintermediate level (2.0 g l−1) resulted in 160–170 mg l−1 S1(Fig. 3b). A narrow region of optimal conditions was foundat the trace elements level 0 (10 ml l−1) with 160–170 mg l−1

of S1 (Fig. 3c). Optimal peptone concentration conditionswere around the centre point (8.5 g l−1) (Fig. 3d) pro-ducing S1 in the range 160–170 mg l−1. Similar contourmaps were used to identify optimum oily precursor con-centrations and interactions with other factors (data notshown).

To confirm the optimal conditions (seeTable 4), a set offour replicate experiments with the optimal combination ofsubstrates and concentrations were used as confirmation ofthe forecasted production of S1. The confirmatory experi-ments produced a titre of 434 mg l−1 S1. This representeda titre 12% higher than the forecasted amount (387 mg l−1).This medium also resulted in 24% more than the highest titrereported of squalestatin S1 in the literature of 350 mg l−1 [16].I yg n thel

4. Discussion

To achieve the results obtained in this study using afull factorial design would have required 313× 2 replicates(3.2× 106) experiments taking into account all the variablesinvolved. However, by using the orthogonal matrix design, asignificantly smaller combination of factors and levels couldbe used for effectively examining the effect of interactingfactors on final yield. Thus, with only a limited number ofexperiments (54), an optimal medium composition was foundthat represented a 42% increase in titre compared to thenon-optimized medium. The S1 titre achieved in this workof 387 mg l−1 represented a significant improvement whencompared to the highest reported in the literature[14]. More-over, a considerable improvement in productivity (50%) wasachieved because the new medium was designed to achievea more rapid S1 production with suitability for industrial ap-plications.

These kinds of designs have been successfully appliedto improving media formulation for the production of pri-mary and secondary metabolites in fermentation processes[10,11,15,16]. For example, yields of another cholesterol-lowering drug, lovastatin, produced byAspergillus terreus,were improved with Plackett–Burman screening factorial de-signs[17]. However, there are no reports of a combined or-thogonal design and contour surfaces methodology as far asw gs.

signa l con-

n terms of productivity (mg l−1 day−1), the present studave S1 production twice as high as the best reported i

iterature[1–3,14].

e know, in the optimization of cholesterol-lowering druThe combined statistical strategies of orthogonal de

nd surface response succeeded in predicting the optima

710 R. Parra et al. / Enzyme and Microbial Technology 37 (2005) 704–711

Fig. 3. Contour diagram of squalestatin S1 generated by the model as a function of: (a) oily precursors; (b) nitrogen; (c) trace elements; and (d) peptone vs.carbon sources. The coded values for oily precursor (OP): soybean oil (−1), cottonseed oil (0) and cottonseed flour (+1); trace elements (TE): 5 ml l−1 (−1),10 ml l−1 (0) and 15 ml l−1 (+1); nitrogen (N): 1 g l−1 (−1), 2 g l−1 (0) and 3 g l−1 (+1); peptone (Pep): 5 g l−1 (−1), 10 g l−1 (0) and 15 g l−1 (+1); and sourcesof carbon (SC): glycerol (−1), glucose (0) and glucose replicate (+1), respectively.

dition and interactions between substrate type and concentra-tions. This work demonstrated the efficacy of the combinedmethodology achieving the maximum squalestatin S1 titrereported in liquid medium. It produced a rapid screening andreliable methodology for the optimization of medium design.The contour surface methodology was a good tool to separatethe effect of interactions and to find the optimal conditionsbetween two factors. Evaluating the contour surface method-ology provided a better understanding of the interactions be-tween the fermentation medium substrates and their effectson S1 titre. TheR2 values of all parameters gave a good fitbetween the model and the experimental data. The contourplots facilitated easy location of optimal parameters. Thiscombined approach for medium optimisation of S1 produc-tion by thePhoma species has not been reported previously.

This study incorporated the use of a dummy factor (glu-cose) as an in situ evaluation of the reproducibility and robust-ness of the experiment. This enabled comparisons to be madebetween results with the glucose treatment and replicate toevaluate consistency of effects on S1. Glucose was also foundto be a better substrate for the production of squalestatin S1

when compared to glycerol. However, only at low concen-trations did glucose produce high titres. This suggests thatcatabolic repression of glucose occurs at higher levels[18].Thus, under optimal conditions, it was possible to surpass thehighest squalestatin S1 titre so far reported in liquid fermen-tation.

Together, this leads to the conclusion that a combined strat-egy of orthogonal design and contour surface methodology isan excellent means for fermentation medium optimization inthe production of pharmaceutical compounds and enzymes.From an economic point of view, the most important parame-ter for screening and optimization of media are time and cost.The combined strategy demonstrated advantages in compar-ison with traditional methods.

The yeast extract precursor and the cottonseed flour oilyprecursor were very important components of the mediumfor the production of squalestatins and this is in agreementwith previous reports[1,3,14,19,20]. Trace element, sodiumcitrate and lactose concentration requirements were at lev-els similar to those in the literature[21–23]. However, themedium required the addition of some important new com-

R. Parra et al. / Enzyme and Microbial Technology 37 (2005) 704–711 711

ponents such as nitrogen, magnesium and iron as part of theimproved medium for enhancing S1 titres. In addition toestablishing optimal fermentation medium composition forscale up, the present work makes it possible to predict bothtitres and productivity under different conditions by means ofthe contour surfaces and the polynomial model. This is use-ful not only for the additional knowledge supplied about theprocess but also for the potential in medium engineering andevaluation under economic constrains of medium composi-tion, yield and productivity. It is also possible to examine theeconomics of scale up of the process from flask to bioreactor.Future work on scale up of the squalestatin S1 fermentationprocess in bioreactors will be evaluated to examine largerscale production and the impact on titres/productivity.

Acknowledgments

We are grateful to the National Council of Science andTechnology (CONACYT) of Mexico for financial supportand to GlaxoSmithKline for supply of the strain and the S1standard.

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