medium optimization for lipid production through co-fermentation of glucose and xylose by the...

Research Paper

Medium optimization for lipid production throughco-fermentation of glucose and xylose by the oleaginousyeast Lipomyces starkeyi

Xin Zhao1, 3, Xiangli Kong2, Yanyan Hua1, Bin Feng2, Zongbao (Kent) Zhao1

1 Division of Biotechnology, Dalian Institute of Chemical Physics, CAS, Dalian, China2 College of Life Science, Liaoning Normal University, Dalian, China3 Graduate School of the Chinese Academy of Sciences, Beijing, China

Co-fermentation of lignocellulose-based carbohydrates is a potential solution to improve the economics ofmicrobial lipid production. In the present paper, experiments were performed to optimize the mediacomposition for lipid production by the oleaginous yeast Lipomyces starkeyi AS 2.1560 through co-fer-mentation of glucose and xylose (2 : 1 wt/wt). Statistical screening of nine media variables was performedby a Plackett–Burman design. Three factors, namely mixed sugar, yeast extract and FeSO4, were found assignificant components influencing cellular lipid accumulation. Further optimization was carried out usinga Box–Behnken factorial design to study the effects of these three variables on lipid production. A math-ematical model with the R2 value at 96.66% was developed to show the effect of each medium compositionand their interactions on the lipid production. The model estimated that a maximal lipid content of61.0 wt-% could be obtained when the concentrations of mixed sugar, yeast extract and FeSO4 were at73.3 g/L (glucose 48.9 g/L, xylose 24.4 g/L), 7.9 g/L and 4.0 mg/L, respectively. The predicted value wasin good accordance with the experimental data of 61.5%. Compared with the initial media, the optimizedmedia gave 1.59-fold and 2.03-fold increases for lipid content and lipid productivity, respectively.

Keywords: Co-fermentation / Lipomyces starkeyi / Microbial oil / Oleaginous yeast / Response surface methodology

Received: September 16, 2007; accepted: December 3, 2007

DOI 10.1002/ejlt.200700224

Eur. J. Lipid Sci. Technol. 2008, 110, 405–412 405

1 Introduction

Some microorganisms have a greater propensity to convertcarbohydrates and other substrates into intracellular lipids.Microorganisms ranging from bacteria, yeasts, molds andalgae can accumulate lipids above 20% of their biomass, espe-cially in oleaginous yeasts and molds [1]. A few yeast species,such as Rhodosporidium sp., Rhodotorula sp., Lipomyces sp. andYarrowia lipolytica, can accumulate intracellular lipids as highas 50% of their cell dry weight. The majority of these lipids aretriacylglycerols containing long-chain fatty acids of 14–20 carbon atoms, when carbohydrates were applied as feed-stock [2]. It is generally recognized that microbial oil can beobtained using cheap substrates such as whey, crop residues,

crude glycerol, crude fats, or even pyrolysis oils [3, 4]. Moreimportantly, microbial lipids can be produced in closed manu-facturing systems with no extensive arable land requirement. Inthis regard, it is pivotal for a sustainable biodiesel industry [5, 6]and of great potential for industrial biotechnology [7].

There are many ways that can drastically improve techno-economics of microbial oil production processes. One possi-bility is to engineer oleaginous strains with modern molecularbiology techniques to improve the capacity of lipid storage orto generate lipids with rare fatty acid profiles [8]. Explorationof lignocellulose-based carbohydrates and other waste mate-rials as substrates may also greatly lower the costs. Lig-nocellulosic materials represent the largest reservoir of poten-tially fermentable carbohydrates. However, these carbohy-drates, obtained either via an acid-promoted hydrolysis or byan enzymatic hydrolysis, are intrinsically a mixture of pentose(e.g. xylose and arabinose) and hexose (e.g. glucose and man-nose, etc.) [9]. For example, complete hydrolysis of corn stalkwill ideally generate a solution primarily containing glucose

Correspondence: Zongbao (Kent) Zhao, Dalian Institute of ChemicalPhysics, CAS, Dalian 116023, China.E-mail: [email protected]: 186 411 84379211

© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.ejlst.com

406 X. Zhao et al. Eur. J. Lipid Sci. Technol. 2008, 110, 405–412

and xylose at a ratio around 2 : 1 (wt/wt). As most traditionalfermentation processes use starch-based raw materials, effec-tive co-fermentation of pentose and hexose is currently notwell established. Therefore, co-fermentation of mixed sugarwill not only significantly improve the overall economics formicrobial lipid production, but also hold an interesting routefor effective biomass conversion.

Lipomyces starkeyi was first reported during studies ofnitrogen-fixing organisms in soils in the middle of the lastcentury [10]. Many species of L. starkeyi were known toaccumulate huge amounts of neutral lipids under variousconditions [11, 12]. The biochemical basis of lipid accumula-tion is believed to result from an imbalanced metabolismunder a carbon-rich and nitrogen-deficient environment whilede novo lipid biosynthesis occurs [2]. When hydrophobicmaterials are used as substrates, lipid accumulation is a pri-mary anabolic activity occurring simultaneously with biomassformation [3]. Previous studies by others and our laboratorydemonstrated that Lipomyces starkeyi could convert xylose tolipids [13, 14]. As the lipid contents of oleaginous yeast aregenerally influenced by media components, especially thecarbon source, the nitrogen source, and minerals, it is thuslogical to search for good conditions for lipid production byco-fermentation of glucose and xylose.

Designing the fermentation media is critical in bio-transformation with live microbes. The medium affects theproduct yield, the volumetric productivity and the economics.Different types of statistical methods are available for mediumcomposition optimization [15]. Response surface methodol-ogy (RSM) is an affective statistical technique that is currentlyused for biotechnological and industrial process optimization[16–18].

The objective of this study was to optimize medium fac-tors for lipid production by L. starkeyi AS 2.1560, through co-fermentation of glucose and xylose at a fixed ratio of 2 : 1 (wt/wt). This work would provide valuable information for furtherresearch on microbial lipid production using lignocellulosehydrolysate as feedstock.

2 Materials and methods

2.1 Microorganism and media

Lipomyces starkeyi AS 2.1560 from the China General Micro-biological Culture Collection Center (CGMCC) was usedthroughout this study. The yeast strain was maintained at 4 7Con yeast peptone dextrose (YPD) agar slants (glucose 20 g/L,peptone 10 g/L, yeast extract 10 g/L, agar 20 g/L) and sub-cultured twice a month [19]. The seed culture medium con-tained (g/L): glucose 20, peptone 10, yeast extract 10, pH 6.0.Original medium contained (g/L): glucose 46.7, xylose 23.3,yeast extract 0.5, (NH4)2SO4 2.0, KH2PO4 7.0, Na2HPO4

2.0, MgSO4?7H2O 0.5, pH 6.0. All media were autoclaved at121 7C for 15 min.

All chemicals were obtained from local suppliers and wereof analytical reagent grade. The yeast extract and corn slurryused in this study contained total nitrogen at 6.0 and 6.4 wt-%,respectively.

2.2 Culture conditions

Shaking-flask cultures were carried out in 250-mL Erlen-meyer flasks containing 50 mL medium. The cultures wereinitiated with 10% (vol/vol) of 25-h-old seeding cultures(2.06106 to 3.06106 cells/mL), and incubated in a rotaryshaker at 200 rpm, 30 7C, for 5 days.

2.3 Analytical methods

Total sugar was analyzed by the dinitrosalicylic acid method[20]. The glucose concentration was determined using a SBA-50B glucose analyzer (Shandong Academy of Sciences,China). The xylose concentration was obtained by subtractingthe glucose value from the total sugar value.

The total lipid was extracted as described [21]. Briefly, wetcells from 20 mL culture broth were harvested by centrifuga-tion, washed twice with distilled water and dried at 105 7C toconstant weight to give the cell biomass. In parallel, wet cellsfrom the same volume of medium were digested with 4 MHCl at 78 7C for 1 h before extraction with chloroform/meth-anol (1 : 1 vol/vol). The extracts were washed with 0.1% NaCland distilled water, dried over anhydrous Na2SO4, evaporatedin vacuo, and the residue was dried at 105 7C overnight to givethe total cellular lipid. The lipid content was expressed by thepercentage of lipid in the cell biomass.

Fatty acid composition analysis was carried out on a7890F gas chromatography instrument (Techcomp ScientificInstrument Co. Ltd., Shanghai, China) according to a pub-lished procedure [22].

2.4 Experimental design

The Plackett–Burman design was used to screen importantmedium components with respect to their effects on the lipidcontent [23]. A total of nine components (variables, k = 9)were selected for this study; each variable was represented attwo levels, high concentration (1) and low concentration (2)(Table 1). According to the Plackett–Burman design, twelvetrials were performed with the lipid content (Y) as the re-sponse (Table 2).

RSM using the Box–Behnken factorial design was appliedto further develop mathematical correlations between threeindependent variables on lipid production [24]. The threevariables were carbon source (glucose and xylose, 2 : 1) (X1),nitrogen source (yeast extract) (X2), and inorganic salt(FeSO4) (X3). Low, medium and high levels of each variablewere designed as –1, 0 and 11, respectively, as shown inTable 3. Table 4 gives the experimental design including theactual response (Y) value. The response variable was fitted by


Eur. J. Lipid Sci. Technol. 2008, 110, 405–412 Co-fermentation of glucose and xylose by oleaginous yeast 407

Table 1. Variables and their levels employed in the Plackett–Bur-man design.§

Factor Coded symbols Level

–1 1

Glu1Xyl [g/L]{ X1 70 87.5YE [g/L]{ X2 6 7.5NH4Cl [g/L] X3 2 2.5MgSO4?7H2O [g/L] X4 1 1.25CoCl2 [mg/L] X5 0.06 0.075CuSO4 [mg/L] X6 0.18 0.225FeSO4 [mg/L] X7 6 7.5MnSO4 [mg/L] X8 0.14 0.175ZnSO4 [mg/L] X9 8 10

§ All media contained 7 g/L KH2PO4 and 2 g/L Na2HPO4.{ Glu1Xyl: Glucose and xylose at a mass ratio of 2 : 1.{ YE: yeast extract.Culture conditions: growth in flasks, T = 30 7C, initial pH = 6.0,incubation time 5 days.

a second-order model in order to correlate the response vari-ables to the independent variables. The general equation ofthe second-degree polynomial equation is:

Y ¼ b0 þX

bixi þX

bijxixjþX

biix2i (1)

Where Y is the predicted response (lipid content, %); b0 theintercept, bi the linear coefficient, bij the quadratic coefficient,bii is the linear-by-linear interaction between xi and xj regres-sion coefficients, and xi, xj are input variables that influencethe response variable Y.

The accuracy and general ability of the above polynomialmodel (Eq. 1) was evaluated by the coefficient of determina-tion R2, and its statistical significance was determined by anF-test. The significance of the regression coefficients was

tested by a t-test. The regression or response surface regres-sion (RSREG) procedure of Statistical Analysis System wasused to compute the estimated ridge of the optimum re-sponse for increasing radii from the center of the origindesign.

3 Results and discussion

3.1 Nitrogen source optimization

It is known that lipid production requires medium with excesssugar or similarly metabolized components (e.g. glycerol,polysaccharides) and limited other nutrients, usually nitrogen.Thus, the oleaginous profile is critically affected by the car-bon-to-nitrogen molar ratio (C/N) of the culture and otherfactors like aeration, inorganic salts, etc. [2]. In this regard,nitrogen sources deserve a separate treatment before system-atic screening of other medium variables.

An early study with L. starkeyi AS 2.1560 using glucoseas sole carbon source showed that this strain preferred togrow at 30 7C and that lipid and lipid content up to 14.2 g/Land 60.3%, respectively, could be attained under optimalflask culture conditions [25]. To improve cell growth andlipid accumulation using the mixed sugar as carbon source,different nitrogen sources, including yeast extract (nitrogen6.0%), corn slurry (nitrogen 6.4%), urea, NH4Cl or(NH4)2SO4, were employed individually as a substituent forthe nitrogen source in the original medium. The substitutenitrogen source was added such that the initial C/N ratio wasat 40 for all cultures. As shown in Fig. 1, yeast extract andNH4Cl were the most effective nitrogen sources. The bio-mass and lipid content in the medium supplemented withyeast extract and NH4Cl were 17.7 g/L, 44.6% and 14.7 g/L,44.9%, respectively, much higher than those with othernitrogen sources.

Table 2. Plackett–Burman experimental design matrixes and the response values.§

RUN X1 X2 X3 X4 X5 X6 X7 X8 X9 Y

1 87.5 6 2.5 1 0.06 0.18 7.5 0.175 10 36.52 87.5 7.5 2 1.25 0.06 0.18 6 0.175 10 42.13 70 7.5 2.5 1 0.075 0.18 6 0.14 10 50.24 87.5 6 2.5 1.25 0.06 0.225 6 0.14 8 41.55 87.5 7.5 2 1.25 0.075 0.18 7.5 0.14 8 35.66 87.5 7.5 2.5 1 0.075 0.225 6 0.175 8 46.07 70 7.5 2.5 1.25 0.06 0.225 7.5 0.14 10 47.98 70 6 2.5 1.25 0.075 0.18 7.5 0.175 8 45.39 70 6 2 1.25 0.075 0.225 6 0.175 10 42.610 87.5 6 2 1 0.075 0.225 7.5 0.14 10 30.911 70 7.5 2 1 0.06 0.225 7.5 0.175 8 47.212 70 6 2 1 0.06 0.18 6 0.14 8 50.3

§ Culture conditions: growth in flasks, T = 30 7C, initial pH = 6.0, incubation time 5 days.



Table 3. Variables and their levels used in the central compositedesign.§

Factor Level

low medium high

Glu1Xyl (X1) [g/L] 50 (–1) 70 (0) 90 (1)YE (X2) [g/L] 6 (–1) 8 (0) 10 (1)FeSO4 (X3) [mg/L] 4 (–1) 6 (0) 8 (1)

§ All media contained 7.0 g/L KH2PO4, 2.0 g/L Na2HPO4 and 0.5 g/LNH4Cl. For other medium information, see Table 1.

Table 4. Box–Behnken experiment design matrixes and the re-sponse values.§

Run X1 X2 X3 Y Biomass [g/L] Lipid [g/L]

1 50 6 6 38.9 15 5.92 50 10 6 44.4 15 6.73 90 6 6 49.1 18 8.84 90 10 6 52.6 19 105 70 6 4 59.3 18 10.76 70 6 8 53.9 18 9.77 70 10 4 55.1 17 9.48 70 10 8 58.6 17 109 50 8 4 54.7 12 6.610 90 8 4 61.4 19 11.711 50 8 8 49.3 14 6.912 90 8 8 58.3 20 11.713 70 8 6 52.8 17 914 70 8 6 51.5 17.5 915 70 8 6 52.5 17.5 9.2

§ Culture conditions: growth in flasks, T = 30 7C, initial pH = 6.0,incubation time 5 days.

In our previous study, some mineral salts, including ZnSO4,MgSO4, CoCl2, CuSO4, FeSO4 and MnSO4, were also testedfor their effects on lipid production; it was found that a properconcentration of these mineral salts can improve cell growthand lipid accumulation (data not shown). With this informa-tion in hand, we next carried out statistical screening by thePlackett–Burman design (vide infra).

3.2 Medium component screening by Plackett–Burman design

In the Plackett–Burman design, nine medium components,i.e. mixed sugar (glucose and xylose, 2 : 1), yeast extract,ZnSO4, MgSO4?7H2O, CoCl2, CuSO4, FeSO4, MnSO4 andNH4Cl, were selected.

Table 2 summarizes the lipid contents obtained from thePlackett–Burman experimental design for 12 trials with twolevels of concentration for each variable. These componentswere screened at the confidence level of 90% on the basis oftheir effects, because a factor would be unimportant if the

confidence level was below 90%. Table 5 represents the T-value, p-value and the confidence level of each component. Itis clear that the confidence level of components ZnSO4,CoCl2, CuSO4 and MnSO4 were below 90%, suggesting thatthese factors have no significant influence on lipid production.Therefore, these mineral salts were omitted during furtheroptimization. On the other hand, if the component showedsignificance above 90% confidence level, the correspondingcomponent should be effective in lipid production. The largerthe magnitude of the T-value and the smaller the p-value, themore significant was the coefficient. Values of p ,0.1 indicatemodel terms to be significant. The positive sign (1) for theeffective component suggested that further optimization pre-ferred a higher value than the indicated high value, and viceversa.

As the confidence level of NH4Cl equals 90%, 0.5 g/LNH4Cl was included in all ongoing experiments. AlthoughMgSO4?7H2O (variable X4) was found insignificant at theconcentration tested here, our previous experiments demon-strated that it was effective to improve lipid accumulation [25].Specifically, when MgSO4?7H2O was employed at a con-centration of 0.5, 1.0 and 1.5 g/L, the cellular lipid contentsreached 41.6, 56.1 and 51.9%, respectively. Therefore, furthercultures were added with 1.0 g/L MgSO4?7H2O. The rest ofthe components, mixed sugar, yeast extract and FeSO4, showconfidence levels at 98.4, 92.4 and 95.5%, respectively, indi-cating that these factors are significant. So the optimizedconcentration for mixed sugar, yeast extract and FeSO4 wereset as 70 g/L, 8.0 g/L and 6.0 mg/L, respectively.

3.3 Optimization of medium components by Box–Behnken factorial design

A Box–Behnken factorial design with three factors and threelevels, including three replicates at the center point, was car-ried out for fitting a second-order response surface. Threeindependent variables, i.e. mixed sugar-X1, yeast extract-X2

and FeSO4-X3, at three different levels are indicated inTable 3. A set of 15 experiments was performed. All variableswere taken at a central coded value as zero. The minimum andmaximum ranges of variables were used, and the full experi-mental plan with respect to their coded form and the result arelisted in Table 4.

The average of the lipid content was taken as the depend-ent variable or response (Y). The statistical analysis of theexperimental data is listed in Table 6. The coefficient ofdetermination (R2) was calculated as 0.9666, indicating thatthe statistical model can explain 96.66% of the variability inthe response. It is known that the R2 value was always between0 and 1 and that the closer the R2 value was to 1.0, the strongerwas the model, and the better it predicted the response [26].The predicted R2 of 0.9666 was in reasonable agreement withthe adjusted R2 of 0.9065. This indicated a good agreementbetween the experimental and predicted values for the lipidcontent. The adjusted R2 corrected the R2 value for the sample



Figure 1. Effect of nitrogen resourceson biomass and cellular lipid content ofL. starkeyi AS 2.1560. Culture condi-tions: growth in flasks, T = 30 7C, initialpH = 6.0, incubation time 5 days.

Table 5. Regression coefficients and their significances for cellularlipid content from the results of the Plackett-Burman design.

Variable T-value p-value Confidence [%] Importance

X1 –7.9444 0.0155 98.45 1X2 3.4181 0.0760 92.40 3X3 2.9187 0.1001 89.99 4X4 –0.95208 0.4415 55.85 7X5 –2.3256 0.1456 85.44 6X6 –0.60871 0.6046 39.54 8X7 –4.5731 0.0446 95.54 2X8 0.51506 0.6578 34.22 9X9 –2.4504 0.1339 86.61 5

Table 6. Fit statistics for Y.

Y

Mean 52.87R-square 96.66%Adjust-R-square 90.65%RMSE 1.792CV 3.3921

CV, Coefficient of variance%; RMSE, root mean-square error.

size and for the number of terms in the model. If there aremany terms in the model and the sample size is too small, theadjusted R2 may be noticeably smaller than R2.

Table 7 shows the analysis of variance of the resultsobtained from the Box–Behnken design experiments withdifferent initial values of mixed sugar, yeast extract and

Table 7. Analysis of variance for the fitted quadratic polynomialmodel for optimization of cellular lipid content.

YSource DF SS MS F-value Prob . F

x1 1 145.0956 145.0956 45.1781 0.001104{

x2 1 11.5681 11.5681 3.6019 0.11618x3 1 13.4421 13.4421 4.1854 0.096151x1*x1 1 42.2761 42.2761 13.1634 0.01509{

x1*x2 1 0.9025 0.9025 0.2810 0.618728{

x1*x3 1 1.4042 1.4042 0.4372 0.53771x2*x2 1 24.9840 24.9840 7.7792 0.038485{

x2*x3 1 19.7136 19.7136 6.1382 0.05601x3*x3 1 184.8861 184.8861 57.5676 0.000631{

Model 9 464.9669 51.6630 16.0862 0.003486Error 5 16.0582 3.2116Total 14 481.025

df, Degree of freedom; SS, sum of squares; MS, mean square.{ Significant at 1% level.{ Significant at 5% level.

FeSO4. The p-values are used as a tool to check the signifi-cance of each coefficient, which also indicate the interactionstrength between each independent variable. The smaller thep-values are, the bigger is the significance of the correspond-ing coefficient [27]. It indicates that the regression coefficientsof the linear term x1 and the quadratic coefficients of x3

2 aresignificant at the 1% level, and the quadratic coefficients of x1

2,x2

2 and one cross-product (x1, x2) were also significant at the5% level. The final response equation that represented a suit-able model for the lipid content based on Eq. (1) is givenbelow:



Y = 52.25 1 4.25875x1 1 1.2025x2 – 1.29625x3 – 3.38375x12 –

0.475x1x2 1 0.5925x1x3 – 2.60125x22 1 2.22x2x3 1 7.07625x3

2

(2)

The model (Eq. 2) indicates that the substrate (x1) has a sig-nificant effect (p ,0.001) on Yas it had the largest coefficientfollowed by FeSO4 (x3) and yeast extract (x2). The positivecoefficient of x1 and x2 indicated a linear effect to increase thelipid content (Y). Two cross-products (x1, x3 and x2, x3) andquadratic coefficients of x3

2 had positive effects; however, thelinear term x3, one cross-product (x1, x2) and the quadraticcoefficients of x1

2, x22 had negative effects that decrease Y.

Based on the high statistical significance of the regressiondescribed in Tables 6 and 7, this mathematical model was usedto generate the response surface plot (Fig. 2) and to calculatethe conditions under which the lipid content could be carriedout at the optimal condition.

The RSM model indicated that the optimal condition forthe lipid content of L. starkeyi AS 2.1560 corresponded to amixed sugar of 73.3 g/L (scaled x1 value of 0.17), 7.9 g/L ofyeast extract (scaled x2 value of –0.05) and FeSO4 at 4.0 mg/L(scaled x3 value of –0.98). According to the model, theseconditions are likely to provide the best process response,leading to the maximum lipid content of 61.0%.

3.4 Validation of the model

The model was tested by carrying out experiments in shakingflasks with the predicted medium compositions. Table 8 sum-marizes the results with both the optimal medium and theinitial medium. The experimental value was found to be veryclose to the predicted value of the lipid content (61.0%), sug-gesting that the model was successful for guiding lipid co-fer-mentation of glucose and xylose by L. starkeyi AS 2.1560.

Figure 2. Lipid content on 3-D graphics for response surfaceoptimization versus mixed sugar and yeast extract.

Table 8. Lipid fermentation results with the fore-and-aft culturemedia.§

Biomass [g/L] Lipid [g/L] Lipid content [%]

Initial media 16.0 6.2 38.8Optimum media 20.5 12.6 61.5

§ Culture conditions: growth in flasks, T = 30 7C, initial pH = 6.0,incubation time 5 days.

From the RSM design and validation experiments, weobtained the optimal medium composition: mixed sugar73.3 g/L (glucose 48.9 g/L, xylose 24.4 g/L), yeast extract7.9 g/L, FeSO4 4.0 mg/L, KH2PO4 7.0 g/L, Na2HPO4 2.0 g/L, MgSO4?7H2O 1.0 g/L, and NH4Cl 0.5 g/L. Flask culturewith this medium was performed to validate the model, i.e. thepredicted lipid content of 61.0% under these conditions. Theexperimental value was 61.5%, which agreed well with thepredicted data. Compared with the initial medium, biomass,lipid and lipid content of the optimum medium increased1.28-, 2.03- and 1.59-fold, respectively. As a result, thedeveloped models were considered accurate and reliable topredict lipid fermentation by L. starkeyi AS 2.1560 with mixedsugar. The kinetics of glucose and xylose evolution duringgrowth in the optimized medium was also studied, and itshowed that glucose and xylose consumption was apparentlysequential. During the early stage (0–72 h),the glucose con-centration dropped from 48.9 to 3.5 g/L, while xylose didfrom 24.5 to 20.1 g/L, suggesting that glucose and xyloseuptake were at 0.63 and 0.06 g/(L h), respectively. Xyloseuptake increased drastically to 0.27 g/(L h) in the late stage(72–120 h). This substrate consumption profile was in goodaccordance with the inhibitory effect of glucose on microbialgrowth [28].

Different substrates and cultivation modes have beenemployed to maximize microbial lipid production with olea-ginous yeasts. Some representative results are summarized inTable 9. When glucose was used as substrate in fed-batchmode, results with high biomass of over 100 g/L were docu-mented [22, 33]. Fed-batch mode with glycerol as substratealso reached a biomass of 100 g/L [31]. When continuousculture or simple batch culture was operated for microbiallipid fermentation, biomass was usually much lower due tolimited substrate availability [3, 4, 29, 30, 32, 34]. The cellularlipid content is another important aspect for lipid production.Fewer examples reached lipid contents higher than 50% [22,29, 32]. When xylose was used as feedstock in continuousculture mode, biomass and lipid content were 15 g/L and37%, respectively [34]. In this work a higher lipid content of61.5% was obtained with batch flask mode, which is appar-ently higher than those using the continuous and batch culti-vation modes listed in Table 9. More significantly, the sugarmixture of glucose and xylose tested herein was reminiscent ofa prototype of lignocellulose hydrolysate.



Table 9. Lipid production of yeasts grown on various substrates in different cultivation modes.

Yeasts Substrate Cultivation mode Biomass [g/L] Lipid [g/L] Lipid content [%] Reference

C. curvatus Glycerol Fed-batch 91 29.1 32 [31]C. curvatus Glycerol Fed-batch 118 29.5 25 [33]R. toruloides Glucose Fed-batch 106.5 71.9 67.5 [22]C. curvata Glucose Continuous 13.5 3.9 29 [34]C. curvata Xylose Continuous 15 5.6 37 [34]Y. lipolytica Industrial glycerol Continuous 8.1 3.5 43 [4]A. curvatum Whey permeates Batch fermentor 19.7 11.4 58 [29]A. curvatum Glucose Batch fermentor 14.5 6.6 45.6 [30]Y. lipolytica Stearin Batch flask 15.2 7.9 52 [32]Y. lipolytica Industrial fats Batch flask 8.7 3.8 44 [3]L. starkeyi Glucose & Xylose Batch flask 20.5 12.6 61.5 This work

It should be noted that the fatty acid composition showedlittle change among the samples obtained in this work. Arepresentative fatty acid profile of the microbial lipids was asfollows: myristic acid 0.5%, palmitic acid 36.6%, palmitoleicacid 4.3, stearic acid 6.2%, oleic acid 48.9%, linoleic acid1.1%, and unknown 2.5%. It was known that oleaginous yeastsmainly produce C16 and C18 series long-chain fatty acidswhen the de novo SCO production occurred [35]. However,fatty acid profiles were largely determined by the substratewhen hydrophobic materials, such as industrial tallow andwaste fats, were used as substrates [3, 32].

4 Conclusions

Medium composition was systematically optimized for lipidproduction through co-fermentation of glucose and xylose(2 : 1, wt/wt) by the oleaginous yeast Lipomyces starkeyi2.1560. Three factors that have remarkable effects on the lipidcontent were identified by Plackett–Burman design experi-ments. These factors are mixed sugar, yeast extract, andFeSO4. Further optimization was done using Box–Behnkenfactorial design. A mathematical model with an R2 value of96.66% was realized which estimated a cellular lipid content of61.0% with the optimal co-fermentation medium. Modelvalidation showed that the predicted values agreed well withthe experimental data. Overall, lipid and cellular lipid contentswere increased 2.03- and 1.59-fold, respectively, compared tothose with the initial medium. This work provided valuableinformation for further investigation of lipid production withmixtures of hexose and pentose.

Acknowledgments

This work was supported by the National Basic Research Programof China (973 Program) (No. 2004CB719703) and the CAS“100 Talents” program.

Conflict of interest statement

The authors have declared no conflict of interest.

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