Modelling the freezing response of baker’s yeast prestressed cells: a statistical approach

ORIGINAL ARTICLE

Modelling the freezing response of baker’s yeastprestressed cells: a statistical approachM.F. Kronberg1, P.I. Nikel1, P. Cerrutti2 and M.A. Galvagno1,2

1 Instituto de Investigaciones Biotecnologicas, IIB-CONICET, Universidad Nacional de San Martın, Buenos Aires, Argentina

2 Departamento de Ingenierıa Quımica, Facultad de Ingenierıa, Universidad de Buenos Aires, Pabellon de Industrias, Ciudad Universitaria, Buenos

Aires, Argentina

Introduction

During its production, application and storage in the bak-

ing industry, baker’s yeast Saccharomyces cerevisiae

encounters different stressful conditions including supra-

optimal temperatures, hyperosmolarity, low internal pH,

ionic toxicity, freezing and oxidation (Attfield 1997; Heg-

gart et al. 1999; Randez-Gil et al. 1999). There is some

degree of commonality in the nature of damage resulting

from the various stresses encountered. For this reason, it

was proposed that common protection and repair mecha-

nisms can provide tolerance to a broad range of environ-

mental insults. In fact, a single sublethal stress could

predispose to protection against subsequent lethal expo-

sure to the same or other types of stresses (Komatsu et al.

1990; Lewis et al. 1995; Thevelein and De Wilde 1999),

a phenomenon called ‘cross-protection’ (Siderius and

Mager 1997).

The damages and tolerance mechanisms induced by

freezing–thawing (FT) are not fully identified. However,

several hypotheses have been proposed to explain the

damage caused by slow freezing at )20�C (3�C min)1)

and thawing. For instance, cells are damaged by physical

changes associated with ice nucleation and dehydration

(Mazur 1970; Franks et al. 1983; Toner et al. 1993). They

can also be affected by changes in intracellular osmolarity

and pH, which lead to aggregation of macromolecules

and denaturation of proteins (Clegg et al. 1982; Franks

et al. 1983). Besides, an oxidative burst takes place during

thawing, with the concomitant generation of reactive oxy-

gen species and damage of cellular components (Park

et al. 1998).

Keywords

experimental design, freeze tolerance,

prestressed baker’s yeast, response surface

methodology, survival.

Correspondence

Miguel A. Galvagno, Laboratorio de

Microbiologıa Industrial, Departamento de

Ingenierıa Quımica, Facultad de Ingenierıa,

Universidad de Buenos Aires, (1428) Buenos

Aires, Argentina. E-mail: [email protected]

2007 ⁄ 0592: received 13 April 2007, revised 8

August 2007 and accepted 24 August 2007

doi:10.1111/j.1365-2672.2007.03588.x

Abstract

Aims: To study the effect of prestress conditions on the freezing and thawing

(FT) response of two baker’s yeast strains and the use of statistical analysis to

optimize resistance to freezing.

Methods and Results: Tolerance to FT of industrial strains of Saccharomyces ce-

revisiae was associated to their osmosensitivity and growth phase. Pretreatments

with sublethal stresses [40�C, 0Æ5 mol l)1 NaCl, 1Æ0 mol l)1 sorbitol or 5%

(v ⁄ v) ethanol] increased freeze tolerance. Temperature or hyperosmotic pre-

stresses increased trehalose contents, nevertheless no clear correlation was

found with improved FT tolerance. Plackett–Burman design and response sur-

face methodology were applied to improve freeze tolerance of the more osmo-

tolerant strain. Optimal prestress conditions found were: 0Æ779 mol l)1 NaCl,

0Æ693% (v ⁄ v) ethanol and 32Æ15�C.

Conclusions: Ethanol, saline, osmotic or heat prestresses increased freezing tol-

erance of two phenotypically distinct baker’s yeast strains. A relationship

among prestresses, survival and trehalose content was not clear. It was possible

to statistically find optimal combined prestress conditions to increase FT toler-

ance of the osmotolerant strain.

Significance and Impact of the Study: Statistically designed combination of

prestress conditions that can be applied during the production of baker’s yeast

could represent a useful tool to increase baker’s yeast FT resistance.

Journal of Applied Microbiology ISSN 1364-5072

716 Journal compilation ª 2007 The Society for Applied Microbiology, Journal of Applied Microbiology 104 (2008) 716–727

ª 2007 The Authors

Apart of being involved in signalling regulation, treha-

lose plays important functions in protecting membranes

and proteins in several organisms against the adverse

effects of stresses, such as heat, cold, anoxia and dissec-

tion, by stabilizing intact cells in the dry state (Crowe

and Crowe 1988, 1990; Elbein et al. 2003; Crowe 2007).

In S. cerevisiae, tolerance to freezing has generally been

correlated with the intracellular trehalose content,

although a direct correlation was not always found above

a certain threshold value (Attfield et al. 1994; Lewis et al.

1997; Sano et al. 1999; Folch-Mallol et al. 2004). Accu-

mulation of other solutes, such as amino acids and glyc-

erol, and expression of aquaporins were also reported to

increase the freezing resistance (Takagi et al. 1997, 2000;

Tanghe et al. 2002; Shima et al. 2003; Alves-Araujo et al.

2004).

Baker’s yeast quality is strongly dependent on physio-

logical conditioning during biomass production. Thus,

increased stress tolerance can be accomplished by manipu-

lating several factors in the propagation protocols; such as

specific growth rate, molasses pulses, osmolytes addition,

fermentation temperature, aeration, etc.; being the basis of

this approach the cross-protection phenomena above

mentioned (Attfield 1997; Galvagno and Cerrutti 2004).

Several works related to physiological conditioning of

baker’s yeast to FT were conducted using the ‘one-factor-

at-a-time’ technique (Lewis et al. 1997; Park et al. 1997,

1998; Alves-Araujo et al. 2004). However, this approach

failed in locating the region of optimum response because

it does not take into account any joint–factor interac-

tions. Statistical experimental designs are preferred as they

can simultaneously consider several factors at many dif-

ferent levels and allow to determine the interactions

among them using a smaller number of experiments.

Also, they can be used to assess the importance of indi-

vidual factors, appropriateness of their functional form,

and sensibility of the response to each factor.

The purpose of this work was to study the response to

FT of two industrial strains of S. cerevisiae representative

of the types of yeast produced by baker’s yeast manufac-

turers (Bell et al. 2001). We also studied the possible rela-

tionship between the enhanced tolerance to FT and

trehalose levels after exposure to mild-stress conditions.

Statistically designed experiments were used to find the

optimal prestress conditions enhancing yeast resistance to

FT of the osmotolerant strain.

Materials and methods

Yeast strains and growth conditions

Saccharomyces cerevisiae strains Y8 (BAFC 3084) and Y9

(BAFC 3083) were kindly provided by CALSA (Companıa

Argentina de Levaduras S.A.) and used throughout this

study. Y8 and Y9 strains are suited for high-sugar and

no-sugar doughs formulations respectively (supplier’s per-

sonal communication). Cells were grown and maintained

in YEP medium [5 g l)1 yeast extract, 5 g l)1 casein pep-

tone, 3 g l)1 KH2PO4, 3 g l)1 (NH4)2SO4, 10 g l)1 glu-

cose].

Inocula, consisting in a loop full of cells from an agar-

ized YEP plate (20 g l)1 agar), were incubated overnight

in YEP at 28�C and 200 rev min)1 in a rotatory shaker.

Working cultures were inoculated at an initial optical

density of 0Æ05 measured at 640 nm (OD640), and incu-

bated as described. Growth phases were monitored by

assaying glucose and ethanol in the culture supernatants,

and cells were tested for FT tolerance after 16–18 h incu-

bation (fermentative phase), 40 h incubation (respiratory

phase or exhaustion of glucose), and 8–10 h after entry

stationary phase (exhaustion of ethanol; Lewis et al.

1993).

Freezing tolerance assay

Cells, harvested by centrifugation at 1500 g for 10 min,

were washed three times by suspension and centrifugation

with equal volumes of 0Æ1 mol l)1 phosphate buffer (pH

6Æ0). After the final washing step, the pellet was sus-

pended at 1 · 107 cells ml)1 in YEP medium without glu-

cose (YEPNG). When necessary, cells suspended in

YEPNG medium were exposed to sublethal stress pre-

treatments before FT. FT stress was performed by expos-

ing 5 ml of the cellular suspension to two cycles of

freezing at –20�C for 20 and 4 h respectively, and thawing

at 25�C. Survival was determined by diluting cells in

phosphate buffer and plating them onto agarized YEP

plates in quadruplicate. FT survival was recorded after

freezing and the percentage of viable cells number

(CFU ml)1) obtained after FT was compared with values

of unfrozen samples.

Sublethal stress pretreatments

Mild-stress pretreatments were performed by incubating

5-ml aliquots of the cell suspension in YEPNG (c. 107

UFC ml)1) as follows: (a) heat-shock, cellular suspension

was incubated at 40�C in a 22 mm-Pyrex tube, and stress

was halted at 0�C; (b) ethanol, 5% (v ⁄ v) ethanol; (c) oxi-

dative, 5Æ0 mmol l)1 H2O2; (d) acid, 0Æ2% (v ⁄ v) acetic

acid; (e) saline, 0Æ5 mol l)1 NaCl; (f) osmotic, 1Æ0 mol l)1

sorbitol. Except for heat-shock, cells subjected to mild-

stresses were incubated at 25�C for 2 h and the stresses

were stopped by washing the cells with phosphate buffer.

Finally, cells suspended in YEPNG, were frozen and

thawed as described above. Mild-stress conditions were

M.F. Kronberg et al. Freezing tolerance of prestressed yeast strains

ª 2007 The Authors

Journal compilation ª 2007 The Society for Applied Microbiology, Journal of Applied Microbiology 104 (2008) 716–727 717

selected to provide a survival greater than 80% (i.e. suble-

thal stresses) after pretreatments.

Vitality assay

Vitality assay was performed by the acidification power

test described by Kara et al. (1988) modified as follows:

cells were harvested by centrifugation at 40–42 h cultiva-

tion and washed three times with phosphate buffer,

then suspended at 1–5 · 107 cells ml)1 in YEPNG, and

exposed to FT stress as described. Cells were washed three

times by suspension and centrifugation with chilled

bi-distilled water and suspended in an equal volume of

bi-distilled water at 25�C. The pH of the yeast suspension

was recorded for 10 min at 1 min intervals using a pH-

meter fitted with temperature compensation. Thereafter,

glucose was added at 10 g l)1 and pH was recorded as for

another 10 min at 1-min intervals.

Analytical procedures

Intracellular trehalose concentration

An aliquot of 5 ml of cell suspension (c. 5 · 107 cells)

was centrifuged, the cell pellet was washed with phos-

phate buffer, frozen immediately in liquid N2 and main-

tained at )80�C until analysis. Intracellular trehalose

concentration was assessed as described by Hounsa et al.

(1998). Briefly, trehalose was extracted from the samples

in 1 ml of boiling 0Æ25 mol l)1 Na2CO3 for 20 min.

Aliquots of 0Æ2 ml were neutralized by adding 0Æ1 ml of

1 mol l)1 acetic acid and 0Æ1 ml of buffer T

(0Æ30 mol l)1 sodium acetate and 0Æ03 mol l)1 CaCl2,

pH 5Æ5). Aliquots of 200 ll of this mixture were incu-

bated with an equal volume of trehalase (Sigma-Aldrich

T-8778, diluted 1 ⁄ 3; Sigma-Aldrich, St Louis, MO) for

1 h at 40�C. The glucose released was assayed as

described below. Trehalose concentration was expressed

as percentage of dry cellular mass (% w ⁄ w). Cell dry

weight was calculated from absorbance values (1 OD640

unit was equivalent to 0Æ59 ± 0Æ02 g l)1 for Y8, and

0Æ73 ± 0Æ03 g l)1 for Y9, by previously running calibra-

tion curves of OD640 vs cell dry weight).

Glucose and ethanol determinations

Glucose and ethanol were determined enzymatically in

aliquots of supernatants of culture broths: glucose was

assayed by the glucose oxidase–peroxidase method

(Wiener Laboratories, Rosario, Argentina) as recom-

mended by the supplier. Ethanol was determined using a

kit provided by Randox (Randox Laboratories Ltd,

Oceanside, CA, USA), based on the reaction with alcohol

dehydrogenase. Standard curves were constructed using

authentic glucose and ethanol solutions.

Statistical experimental designs and statistical analysis

In the screening step, a Plackett–Burman design (PBD), a

two-level multifactorial design based on the rationale

known as balanced incomplete blocks (Stanbury et al.

1986), was used. The key to this technique is forming

various combinations (which are called assemblies) of the

variables to be studied with varying amounts. With PBD,

up to N–1 components can be studied in N assemblies,

where N must be a multiple of four. As the number of

components used in this part of the study was four, a

design consisting of four assemblies was used. PBD

assumes that there are no interactions between the differ-

ent media constituents; then, a linear approach is consid-

ered to be sufficient for screening:

Y ¼ b0 þX

i

bixi i¼ 1; 2;:::;k ð1Þ

where Y is the estimated target function and bi represents

the regression coefficient and b0 the intercept. The main

effect of each factor can be simply calculated as the differ-

ence between the average of measurements made at the

high level (+1) of the factor and the average of measure-

ments at the low level ()1). Linear effects of temperature

(x1), NaCl (x2), ethanol (x3) and time of exposure (x4)

were evaluated.

To optimize the levels of critical factors in the prestress

medium, the response surface methodology was assessed

with a central composite design (CCD) using five coded

levels was applied. CCD (Box and Wilson 1951) and the

mathematical model are derived from orthogonal polyno-

mial fitting techniques. Significance of factorial interac-

tion of variables is considered to arrive at the optimum

condition. CCD is essentially a full 23-factorial design

with its eight cube points, two replications at the centre

point and six star points. Levels corresponding to the

centre and the walls of the cube are designated 0, )1, and

+1. A star point is the one that has an axial distance of

Fa (a = 1Æ682) for one factor and 0 for the other two fac-

tors. Central points provide additional degrees of freedom

for error estimation, which increases power when testing

the significance of effects and the a-value is the distance

of the axial points. A software package (Essential Regres-

sion 2Æ205 and Origin 7Æ5) was used to fit the second-

order model to the independent variables using the

following equation:

Y ¼ b0 þXk

i¼1

bixi þXk

j¼2

Xj�1

i¼1

bijxixj þXk

i¼1

biix2i ð2Þ

where Y is the dependent variable (response variable) to

be modelled, xi and xj are the independent variables

(factors), and b0, bi, bij and bii are the regression coeffi-

Freezing tolerance of prestressed yeast strains M.F. Kronberg et al.


ª 2007 The Authors

cients of the model (intercept, linear, interactive and qua-

dratic coefficients respectively). When it was possible, the

model was simplified by dropping terms, which were not

statistically significant (P > 0Æ05) by an analysis of vari-

ance (anova). The lack-of-fit test was used to determine

whether the constructed model was adequate to describe

the observed data. The R2 statistic indicates the percent-

age of the variability of the optimization parameter that

is explained by the model. Three-dimensional surface

plots were drawn to illustrate the main and interactive

effects of the independent variables on the dependent

ones.

In both PBD and CCD, the response parameters were

the survival percentage and the trehalose content. Also, in

both designs the variable levels Xi were coded as xi

according to eqn (3):

xi ¼Xi � X0

DXii ¼ 1; 2; 3;:::;k ð3Þ

where xi and Xi are the dimensionless (codified) value

and the actual value of an independent variable, respec-

tively, X0 is the real value of an independent variable at

centre point and DXi the step change. All experiments

described in this paper were carried out at least by dupli-

cate and repeated at least three times.

Results

Viability and vitality after freezing and thawing

To investigate the sensitivity to FT stress of both strains,

we studied the maintenance of cell viability and vitality

following freezing stress. Viability after FT was examined

throughout the different phases of growth in batch

cultures using glucose as carbon source. As can be seen

in Fig. 1a, cells improved resistance to FT as they pro-

gressed from the fermentative to the respiratory phase

(implicating dependency on mitochondrial functions),

maintaining the survival values at the stationary phase.

These results are not surprising as yeast cells exiting

from the exponential phase and approaching to station-

ary phase of growth become more resistance to stresses

(Lewis et al. 1993). Similar results were found by Park

et al. (1997) using a laboratory strain of S. cerevisiae.

We also found that response of these industrial strains

to FT was not only growth-phase specific but also strain

dependent. Thus, the osmotolerant strain Y8 was

more resistant to FT than Y9 in all the growth phases

tested.

Given that high quality baker’s yeast production not

only depends on the maintenance of its viability but also

on its physiological status, it was important to determi-

nate yeast vitality or fermentative performance as well.

Hence, both yeast strains at the respiratory phase of

growth were subjected to the vitality assay after FT

(Fig. 1b). The acidification power was calculated by sub-

stracting the pH values of the yeast cell suspension at

20 min from the pH at 10 min (DpH). Upon addition of

glucose to the resting cell suspension, pH decrease was

similar in both strains in nonstressed cultures

(DpH = 1Æ25 and 1Æ28 for Y9 and Y8 respectively). On

the other hand, vitality of Y9 strain after freezing was sig-

nificantly reduced when compared with that of Y8

(DpH = 0Æ39 and 1Æ03 respectively). Moreover, values for

the acidification power test calculated before glucose

addition (i.e. DpH10 min ) DpH0 min), were 0Æ08 and 1Æ02

for Y9 and Y8 respectively. Taken together, these results

indicate that in the nonosmotolerant strain Y9 not only

the capability of cell duplication, but also glycolytic activ-

ity (reflecting fermentation performance) were signifi-

cantly affected by FT. The opposite was true for Y8.

According to the theory of the acidification power test

0

10

20

30

40(a)

(b)

Fermentative Respiratory Stationary

Growth phase

Sur

viva

l (%

)

4·0

4·5

5·0

5·5

6·0

6·5

7·0

0 5 10 15 20 25

pH

Time (min)

Figure 1 (a) Survival percentage after FT of two strains of baker’s

yeast at different growth phases: osmosensitive strain (Y9, open bars),

osmoresistant (Y8, closed bars). (b) Acidification power test at respira-

tory growth phase before (open symbols) and after FT (closed sym-

bols) of Y9 (squares) and Y8 strains (triangles). The results represent

the mean values ± SD of four independent experiments.


ª 2007 The Authors


(Kara et al. 1988), the first slope of the curves (0–10 min)

in Fig. 1b, suggests a higher accumulation of endogenous

energy reserves (glycogen and ⁄ or trehalose) in the osmo-

tolerant strain.

Improving FT resistance by pretreatments with sublethal

stresses

We investigated if the response of baker’s yeast to FT

stress was altered when cells in the respiratory phase (as

is the metabolic stage in fed-batch fermentations used for

baker’s yeast production), were previously exposed to dif-

ferent sublethal stresses. Cells were pretreated with differ-

ent stresses that baker’s yeast could encounter during

its production: hyperosmolar (0Æ5 mol l)1 NaCl or

1Æ0 mol l)1 sorbitol), metabolic (50 lg ml)1 cyclohexi-

mide), supra-optimal temperatures (40�C), and oxidative

(5 mmol l)1 H2O2); before being submitted to FT. As can

be seen in Fig. 2(a), only pretreatments with NaCl, heat,

ethanol or sorbitol improved FT tolerance in both strains.

For example, for the osmotic (sorbitol) prestress survival

values after FT were 89Æ33 and 70Æ68% (compared with

27Æ71% and 7Æ52% for the nonprestressed cells) for Y8

and Y9 respectively.

In S. cerevisiae, trehalose has been positively correlated

with stress tolerance (D’Amore et al. 1991; De Virgilio

et al. 1994; Lewis et al. 1995; Hounsa et al. 1998). In

accordance, we found that nonstressed cells of the FT-

resistant Y8 strain accumulated higher intracellular levels

of the disaccharide in the respiratory phase of growth,

than the less tolerant strain Y9 (4Æ52% vs 2Æ86%), showing

a higher viability values after FT (Figs 1 and 2a). To

investigate a possible relationship between trehalose levels

and FT, intracellular accumulation of this solute was

measured in prestressed cells of both strains before being

exposed to FT. Figure 2(b) also shows that trehalose con-

tent increased to values higher than 5% in Y8 after pre-

treatments with NaCl, heat or sorbitol, been these

increase accompanied by an increase in survival to FT

(Fig. 2a) suggesting a priori a relationship between FT

survival and trehalose accumulation for this strain. How-

ever, ethanol pretreatment, which improved survival in

both strains, did not produce a significant change in tre-

halose levels in Y8 nor in Y9 strains, suggesting that the

ethanol stress response operates through a pathway inde-

pendent from trehalose. Also, in Y9 prestress treatments

(except for ethanol), which improved survival to FT, pro-

duced an increase in trehalose levels attaining values up

to 3Æ5% in a stress-dependent manner (Fig. 2), and an

almost linear relationship between trehalose accumulation

and survival percentage after FT was found (R2 = 0Æ930;

data not shown). A different behaviour was found for the

osmotolerant strain Y8. In this strain, a linear relationship

between trehalose content and FT tolerance was not so

strong (R2 = 0Æ674), but a threshold value of c. 5% treha-

lose appears to be reached to increase FT survival

(Fig. 2b). Higher levels of trehalose were necessary for Y8

to attain survival values similar to those found for Y9

(5Æ61% and 3Æ34% respectively).

Taken together, these results indicate that the contribu-

tion of trehalose to tolerance to freezing is rather incon-

sistent, therefore trehalose content should not be used as

a general predictor of FT tolerance, at least in these

industrial strains.

Optimization of sublethal prestress conditions to

improve FT resistance by experimental designs

methodology

Taking into account that pretreatment with NaCl, heat

or ethanol improved FT tolerance in the strains under

study, it seemed worthwhile to investigate in more

detail the effect of those sublethal prestresses concomi-

tantly in Y8 to improve its FT survival. This strain

showed to be the more promising one to be manipu-

0

1

2

3

4

5

6

Con

trol

Oxi

dativ

e

Aci

dic

Sal

ine

Hea

t

Eth

anol

Osm

otic

0

20

40

60

80

100(a)

(b)

Sub-lethal stress pre-treatment

Sur

viva

l (%

) T

reha

lose

(%

w/w

)

Figure 2 (a) Survival percentage after FT and (b) intracellular treha-

lose content of the two baker’s yeast strains at respiratory growth

phase subjected or not to different sublethal prestresses as described

in Materials and methods. Osmosensitive strain (Y9, open bars), osmo-

resistant (Y8, closed bars). The results represent the mean values ± SD

of four independent experiments.



ª 2007 The Authors

lated physiologically to enhance its freeze tolerance

because after being submitted to certain prestress condi-

tions it could attain higher FT survival values (near to

90%). For this reason, it could be very useful to use Y8

in frozen dough technology.

To assess the relative importance of each sublethal

stress conditions on FT survival and on intracellular accu-

mulation of trehalose, the effect of NaCl, ethanol concen-

trations, temperature as well as the time of exposure to

pretreatments were first evaluated modulating the vari-

ables according to a PBD. Table 1 shows the PBD used,

the coded and the actual levels of the independent vari-

ables and the responses attained. The results show that

survival widely varied in the range from 20% to 100%,

depending on the conditions tested while trehalose levels

did not significantly changed.

Table 2 shows that of the four combined independent

variables only NaCl and ethanol had a relevant influ-

ence, with P < 0Æ05 (i.e. statistical confidence >95%) in

survival to freezing. NaCl combined with ethanol and

heat showed a significant effect on trehalose accumula-

tion. The equations obtained for survival to freezing and

for trehalose accumulation as a function of the actual

variables were:

Survival ð%Þ ¼ 94�886� 0�762 temperature ð�CÞþ 54�392 NaCl ðmol l�1Þ� 7�302 ethanol ð% v=vÞþ 0�019 incubation time ðhÞ

Trehalose ð% w=wÞ ¼ 4�286� 0�047 temperature ð�CÞþ 1�606 NaCl ðmol l�1Þþ 0�166 ethanol ð% v=vÞþ 0�125 incubation time ðhÞ

Regarding survival of Y8 after FT, among the pre-

treatments in the ranges assayed, ethanol had a negative

estimate, meaning that survival decreased as the ethanol

concentrations raised from 1% to 5% (v ⁄ v). On the

contrary NaCl influenced very significantly survival, hav-

ing at the same time a very large and positive estimate.

This means that the percentage of survival to FT

increased as the salt concentration changed from 0Æ2 to

1Æ0 mol l)1.

Saline prestress had a very significant effect on treha-

lose accumulation presenting at the same time the largest

estimate with respect to the other prestress conditions

assayed. The ethanol and temperature effect on the disac-

charide accumulation were slightly significant and pre-

sented a low estimate, meaning that they did not greatly

influence that response. On the other hand, exposition

times from 1 to 3 h to the different mild-stresses had not

a significant effect on survival to freezing nor on trehalose

accumulation.

Fisher’s F-test demonstrated that the model was signifi-

cant (Table 3). The lack of fit of the model was signifi-

cant (P < 0Æ05). This result means that the proposed

equation may not have included all appropriate functions

of independent variables or else that the experimental

Table 1 Plackett–Burman screening design and responses observed for Y8 strain

Trial

Factor level* Response�

Temperature NaCl Ethanol Incubation time Survival Trehalose

1 0� 35 0 0Æ6 0 3 0 2 97Æ21 4Æ28

2 +1 42 )1 0Æ2 )1 1 )1 1 67Æ51 3Æ15

3 )1 28 +1 1Æ0 +1 5 )1 1 88Æ31 5Æ15

4 +1 42 )1 0Æ2 +1 5 +1 3 20Æ51 4Æ33

5 +1 42 +1 1Æ0 +1 5 )1 1 88Æ61 4Æ42

6 )1 28 +1 1Æ0 )1 1 )1 1 82Æ80 5Æ61

7 0 35 0 0Æ6 0 3 0 2 109Æ41 4Æ09

8 )1 28 )1 0Æ2 )1 1 )1 1 92Æ90 3Æ45

9 +1 42 )1 0Æ2 +1 5 )1 1 18Æ82 3Æ97

10 )1 28 )1 0Æ2 +1 5 +1 3 36Æ90 4Æ09

11 )1 28 +1 1Æ0 +1 5 +1 3 98Æ28 6Æ52

12 )1 28 )1 0Æ2 )1 1 +1 3 71Æ88 3Æ64

13 +1 42 +1 1Æ0 )1 1 +1 3 105Æ81 4Æ33

14 +1 42 +1 1Æ0 )1 1 +1 3 105Æ81 4Æ33

15 0 35 0 0Æ6 0 3 0 2 102Æ30 4Æ45

*Temperature in �C, NaCl concentration in mol l)1, ethanol concentration in % (v ⁄ v), incubation time in h.

�Data represent the average of four independent experiments. Survival is expressed as a percentage, trehalose content in percentage of dry cellu-

lar mass.

�Numbers in the first column are the coded values of the variables.


ª 2007 The Authors


region may be too large to use a quadratic model. Box

et al. (1999) pointed out that when large amounts of data

were included in the analysis, a model with significant

lack of fit could still be used. Nevertheless, the higher R2

coefficient and the obtained graphics of normal probabil-

ity for the residuals (data not shown) indicated that the

proposed model properly described the responses.

Based on these results, plots for the responses measured

as a function of two variables were constructed. Figure 3

illustrates the effects of NaCl and ethanol on survival to

FT and trehalose accumulation in Y8 strain.

From a biotechnological point of view, tolerance to FT

is of major importance. Therefore, critical pretreatments

influencing survival to FT were identified via screening

design, and optimal values of those independent variables

were defined to obtain maximum survival and trehalose

accumulation after the prestress treatments. According to

the results obtained from PBD, a CCD was carried out to

optimize NaCl and ethanol pretreatments. In spite of the

fact that we did not find a significant effect of tempera-

ture in PBD, we included this variable as it was consid-

ered an important environmental one and because it

influences trehalose accumulation in this strain. The vari-

able time was kept at its middle level of the factor setting

range (2 h) during pretreatments. Table 4 presents the

actual and coded values for the three significant variables

in the CCD, as well as the responses corresponding to the

percentage of survival and trehalose levels. The best con-

ditions for increasing FT tolerance seemed to be those

near to centre points; i.e. 0Æ9 mol l)1 NaCl, 0Æ7% (v ⁄ v)

ethanol, and 32�C. The best conditions for trehalose accu-

mulation could not be found (data not shown). Data

Table 2 Analysis of the Plackett–Burman screening design for (a) sur-

vival percentage after FT and (b) trehalose content for Y8 strain

Term

Regression

coefficient SE P-value t-value

(a)

Intercept 79Æ137 4Æ442 < 0Æ001 14Æ601

x1 )5Æ333 6Æ062 0Æ394 )0Æ890

x2 21Æ758 6Æ062 0Æ005* 3Æ631

x3 )17Æ607 6Æ062 0Æ035* )2Æ437

x4 0Æ020 6Æ062 0Æ998 0Æ003

(b)

Intercept 4Æ388 0Æ121 < 0Æ001 36Æ215

x1 )0Æ327 0Æ135 0Æ036* )2Æ411

x2 0Æ645 0Æ135 0Æ001* 4Æ761

x3 0Æ330 0Æ135 0Æ035* 2Æ436

x4 0Æ125 0Æ135 0Æ135 0Æ923

Codified values for: x1, temperature; x2, NaCl; x3, ethanol; x4, incuba-

tion time. SE, standard error.

*Significant at P < 0Æ05.

Table 3 ANOVA of the Plackett–Burman screening design for (a) sur-

vival percentage* after FT and (b) trehalose content� for Y8 strain

Source of

variation

Degrees of

freedom

Sum of

squares

Mean

square F-value P-value

(a)

Model 4 8582Æ695 2145Æ674 4Æ865 0Æ019

Residual 10 4410Æ070 441Æ007

Lack of fit 7 4334Æ969 619Æ281 24Æ738 0Æ012

Pure error 3 75Æ100 25Æ033

Total 14 12992Æ765

(b)

Model 4 7Æ767 1Æ942 8Æ818 0Æ002

Residual 10 2Æ202 0Æ220

Lack of fit 7 2Æ137 0Æ305 14Æ109 0Æ026


Total 14 9Æ969

*R2 = 0Æ895, adjusted R2 = 0Æ851, coefficient of variation = 26Æ536.

�R2 = 0Æ883, adjusted R2 = 0Æ789, coefficient of variation = 10Æ694.

–1·0–0·5

0·00·5

1·0

20

40

60

80

100

120

(a)

(b)

–1·0

–0·5

0·0

0·5

1·0

Sur

viva

l (%

)

Eth

anol

0153045607590105120

–1·0–0·5

0·00·5

1·0

2·5

3·0

3·5

4·0

4·5

5·0

5·5

6·0

–1·0

–0·5

0·00·5

1·0

Treh

alos

e (%

)

Ethan

ol

Sodium chloride

Sodium chloride

2·02·53·03·54·04·55·05·56·0

Figure 3 Surface plots for (a) survival after FT and (b) trehalose con-

tent of Y8 strain as a function of ethanol and sodium chloride con-

centration levels for the Plackett–Burman screening design. Contour

plots are depicted at the bottom of the figure.



ª 2007 The Authors

obtained for FT tolerance were analysed by multiple

regression (Table 5). The experimental results of the CCD

were fitted and explained with a second order polynomial

function [eqn (2)]. The significance of each coefficient

was determined by Student’s t-test and P-values, listed in

Table 5. As can be seen, linear and quadratic effects of

NaCl and ethanol, as well as the corresponding interactive

coefficient, were significant for survival, although at dif-

ferent levels.

Table 6 shows the results of anova for CCD. The lack

of fit of the regression model was not significant, and Fi-

scher’s F test demonstrated a high significance for the

regression. The fitness of the model was checked by the

determination coefficient (R2). In this case, the value of

R2 = 0Æ915 indicates that only 8Æ5% of the total variations

are not explained by the model. In addition, the value

of the adjusted determination coefficient (adjusted

R2 = 0Æ837) advocates for a high significance of the model

(Haaland 1987). At the same time, a relatively lower value

of the coefficient of variation (CV = 10Æ887) indicates an

improved precision and reliability of the experiments

(Montgomery 2001).

With the aim to increase FT survival, the most impor-

tant response from a biotechnological point of view, we

further studied the optimization of prestress conditions to

reach that objective. Trehalose accumulation studies were

also carried out.

Response surface plots were obtained as function of

two variables at a time (namely, NaCl and ethanol),

maintaining all other conditions at fixed levels. Percent-

age of survival for different combinations of prestress

conditions can be predicted from these plots (Fig. 4a).

The maximum predicted survival is indicated by the

surface confined in the response surface diagrams as can

be seen from the tri-dimensional response surface and

contour plots (representing the regression equation for

survival after FT), where the effect of pairwise interac-

tion of the two variables is depicted. Figure 4b shows

the response surface for trehalose accumulation, demon-

strating that its dependence on NaCl and ethanol levels

is quite different when compared with that of survival

percentage.

Combination of parameters resulting in optimal sur-

vival to FT was obtained by numerical analysis, solving

the system of partial derivatives (dY ⁄ dx1, dY ⁄ dx2,

dY ⁄ dx3) for the different independent variables. For sur-

vival, the optimal values for the variables in coded units

Table 4 Central composite design and responses observed for Y8

strain

Trial

Factor level* Response�

NaCl Ethanol Temperature Survival Trehalose

1 0� 0Æ9 0 0Æ7 a§ 42Æ73 67Æ84 3Æ68

2 0 0Æ9 0 0Æ7 )a 25Æ27 47Æ89 3Æ35

3 0 0Æ9 0 0Æ7 0 32Æ00 95Æ77 3Æ66

4 a 1Æ4 0 0Æ7 0 32Æ00 59Æ86 3Æ54

5 0 0Æ9 0 0Æ7 0 32Æ00 93Æ78 3Æ67

6 1 1Æ2 1 1Æ2 )1 28Æ00 68Æ84 3Æ11

7 )a 0Æ4 0 0Æ7 0 32Æ00 75Æ82 2Æ91

8 )1 0Æ6 1 1Æ2 )1 28Æ00 75Æ82 3Æ05

9 )1 0Æ6 )1 0Æ2 1 36Æ00 69Æ83 3Æ54

10 0 0Æ9 )a 0Æ0 0 32Æ00 81Æ41 4Æ11

11 0 0Æ9 0 0Æ7 0 32Æ00 90Æ01 3Æ72

12 )1 0Æ6 1 1Æ2 1 36Æ00 83Æ80 3Æ44

13 1 1Æ2 )1 0Æ2 1 36Æ00 61Æ85 3Æ62

14 1 1Æ2 1 1Æ2 1 36Æ00 67Æ84 3Æ52

15 0 0Æ9 a 1Æ5 0 32Æ00 77Æ81 3Æ72

16 )1 0Æ6 )1 0Æ2 )1 28Æ00 89Æ79 3Æ09

*Temperature in �C, NaCl concentration in mol l)1, ethanol concen-

tration in % (v ⁄ v).

�Data represent the average of four independent experiments. Sur-

vival is expressed as a percentage, trehalose content in percentage of

dry cellular mass.

�Numbers in the first column are the coded values of the variables.

§a = 1Æ682.

Table 5 Analysis of the central composite optimization design for

survival percentage after FT for Y8 strain

Term

Regression

coefficient SE P-value t-value

Intercept 92Æ829 4Æ682 <0Æ001 19Æ83

x1 )6Æ303 2Æ199 0Æ024* )2Æ87

x2 0Æ457 2Æ199 0Æ048* 2Æ21

x3 1Æ534 2Æ199 0Æ508 0Æ70

x12 )7Æ723 2Æ420 0Æ015* )3Æ19

x1x2 1Æ670 2Æ873 0Æ037* 3Æ58

x1x3 1Æ420 2Æ873 0Æ636 0Æ49

x22 )3Æ674 2Æ420 0Æ017* )2Æ51

x2x3 3Æ321 2Æ873 0Æ286 1Æ16

x32 )11Æ249 2Æ420 0Æ235 26Æ1

Codified values for: x1, sodium chloride; x2, ethanol; x3, temperature.

*Significant at P < 0Æ05.

Table 6 ANOVA of the central composite optimization design for sur-

vival percentage after FT for Y8 strain

Source of

variation

Degrees of

freedom

Sum of

squares

Mean

square F-value P-value

Model 9 2375Æ696 263Æ966 3Æ998 0Æ041

Residual 7 462Æ154 66Æ022

Lack of fit 5 445Æ037 89Æ007 10Æ400 0Æ090


Total 16 2837Æ850

R2 = 0Æ915, adjusted R2 = 0Æ837, coefficient of variation = 10Æ887.


ª 2007 The Authors


were as follows: x1 = )0Æ404, x2 = )0Æ015 and x3 =

0Æ037; and the actual levels obtained by substituting the

respective values of xi in eqn (1) were: 0Æ779 mol l)1

NaCl, 0Æ693% (v ⁄ v) ethanol and 32Æ15�C. As stated pre-

viously, optimal values of the prestress conditions to

obtain maximal trehalose accumulation were quite dif-

ferent, indicating again that environmental conditions

conducive to optimum survival to freezing did not nec-

essarily parallel those conditions improving trehalose

accumulation.

Validation of the model was based on the optimum

values of NaCl, ethanol and temperature mentioned

above (carried out in four prestress independent experi-

ments). The predicted responses by the model were

93Æ33% for survival and 4Æ03% for trehalose, while the

actual responses were 92Æ59 ± 5Æ83% and 3Æ98 ± 0Æ45%

respectively. The results of the validation experiments

provide further evidence of the model applicability.

Discussion

In this study, we demonstrated that the principle of

cross-protection could be applied to optimize baker’s

yeasts tolerance to freezing by physiological conditioning.

To achieve this goal we used the methodology of experi-

mental designs, which represent a more balanced alterna-

tive to the ‘one-factor-at-a-time’ technique (for a review,

see Strobel and Sullivan 1999).

Preliminary experiments demonstrated that the osmo-

resistant strain was more tolerant to FT, thus confirming

a mechanistic similarity between osmotic and FT stress,

either in the type of injuries caused by the stresses (Hat-

ano et al. 1996; Hernandez-Lopez et al. 2003), or mecha-

nism for repairing damage that is sustained (Lewis et al.

1997). Also, we showed that FT tolerance was higher with

a respiratory metabolism or in the stationary phase of

growth, that are the physiological status of an industrially

manufactured baker’s yeast by a fed-batch protocol

with ⁄ without maturation period.

In commercial use, high quality baker’s yeast must be

able not only to survive to FT but also vitality or baking

performance has to be maintained. We showed that

the acidification power test could be used to assess yeast’s

fermentation performance. This test can be carried out

with a simple laboratory device, thus providing the basis

for a quality control method for selecting the best yeast

strain to resist the different stresses encountered during

its commercial and industrial uses. Moreover, this cheap

test can be conducted in a shorter period of time and

even could replace the time-consuming baking or gassing

power test carried out as a quality control test in baker’s

yeast manufacturing plants.

The importance of trehalose accumulation as a deter-

minant of baker’s yeast stability has been known for a

long time (Gancedo and Flores 2004). Our studies, car-

ried out with two representative strains of industrial

baker’s yeasts (Y8 and Y9) having distinctive phenotypes,

showed that the more freezing resistant (osmotolerant)

strain constitutively presented higher levels of trehalose

and that generally this parallels with increased freeze

resistance (Fig. 1), and our results suggest that trehalose

could play a role in FT. However, while in Y9 (osmosen-

sitive strain) a linear correlation between trehalose accu-

mulation and survival to FT was found, a lack of a strong

correlation between those parameters in Y8 would still

suggest a relationship between trehalose and freeze toler-

ance, even when a linear correlation could not be found.

Our results are in good agreement with those of other

authors, that have also noted inconsistencies between tre-

halose levels and stress tolerance (Gelinas et al. 1989;

Panek et al. 1990; Winkler et al. 1991; Slaugthter and

Nomura 1992; Ratnakumar and Tunnacliffe 2006) and

–2–1

01

2

50

60

70

80

90(a)

(b)

–2

–1

012

Sur

viva

l (%

)

Eth

anol

Sodium chloride

45·0051·2557·5063·7570·0076·2582·5088·7595·00

–2–1

0

1

2–2

–10

122·5

3·0

3·5

4·0

Tr

ehal

ose

(%)

Sodium chloride

Eth

anol

2·502·692·873·063·253·433·623·814·00

Figure 4 Response surface plots for (a) survival percentage after FT

and (b) trehalose content of Y8 strain as a function of ethanol and

sodium chloride concentration levels for the central composite design.

Contour plots are shown at the bottom of the figure.



ª 2007 The Authors

supported the idea that trehalose levels and stress toler-

ance are not strictly related in a linear manner, but a

threshold level of the disaccharide appears to be necessary

for relatively high stress tolerance to freezing (Attfield

et al. 1994; Lewis et al. 1997). This behaviour was exhib-

ited by Y8, as trehalose values higher than 5% were neces-

sary for this strain to present high resistance to FT.

On the other hand, trehalose did not directly contrib-

ute to increase tolerance to FT in the strains studied, as

ethanol pretreatment alone that produced a significant

increase in survival after freezing stress, did not produce

an accompanying increase in trehalose levels. It has not

to be overruled that the combined effect of other factors

apart from trehalose may contribute to stress tolerance in

these strains, thus dominating over the trehalose effect.

Although there was not a consistent relationship between

intracellular trehalose concentrations and freezing toler-

ance, the disaccharide might be recommended but

seemed to be not necessary to increase FT tolerance. The

increase of trehalose endogenous levels by manipulating

culture conditions could improve robustness of baker’s

yeast cells facing different stressing conditions during

industrial manipulation.

Statistical methods used in this study to find prestress

conditions conducive to increase FT tolerance of an

industrial strain of S. cerevisiae allowed us to assess not

only the effect of various prestress conditions separately

but also their interactions. Thus, ethanol pretreatment

applied concomitantly along with heat and NaCl were

conditions that significant increased trehalose levels. On

the other hand, under our experimental conditions

though heat pretreatment assayed alone was effective in

increasing survival to freezing, its influence when assayed

together with those of salt and ethanol pretreatments, was

overlooked against NaCl and ethanol pretreatments.

Moreover, ethanol pretreatment in combination with

temperature and NaCl was statistically significant to

increase freeze tolerance.

Optimal prestress conditions to obtain higher tolerance

to freezing and for trehalose accumulation (as indicative

of general robustness), were determined experimentally by

using response surface methodology. The validity of the

model was proved by fitting the values of the prestress

conditions into a model equation and by carrying out the

experiments using the optimal values of three prestress

conditions (salt, ethanol and heat). Response surface

methodology analysis allowed fine-tuning of the prestress

conditions needed to be applied to our industrial baker’s

yeast strain to increase their tolerance to freezing. We

found that NaCl pretreatment had statistically the greatest

effect in increasing FT tolerance; nevertheless, we estab-

lished also ethanol and temperature conditions to fix

other important process variables in baker’s yeast propa-

gation that be easily manipulated during yeast propaga-

tion even at an industrial scale.

Thus, we demonstrated that the simultaneous applica-

tion of prestress mild conditions [0Æ779 mol l)1 NaCl,

0Æ693% (v ⁄ v) ethanol and 32Æ15�C] for 1–2 h, condition

easy to fulfil during biomass production at an industrial

scale, should be applied to maximize survival of baker’s

yeast strains to FT. These conditions resulted in c. 93%

survival after freezing and thawing, which is much higher

than those obtained in the preliminary experiments, as

well as values reported in the literature (Lewis et al.

1997). We have to stress that although the results

reported herein cannot be directly extrapolated, they con-

stitute a significant approach to improve industrial

baker’s yeast performance in frozen dough technology.

Our results also indicate that the response surface

methodology can be a useful tool to find optimal condi-

tions for yeasts to be used in different industrial biotech-

nological process such as brewing, distiller’s fermentation

and wine making. It could constitute a valid alternative

to genetic improvement thus avoiding the technical diffi-

culties and possible problems with public acceptance of

strains developed using recombinant DNA technology.

Further experiments are now conducted to establish if

the prestress conditions we have found increasing toler-

ance to FT could be applied in a productive process at a

subpilot scale using a fed-batch fermentation employing

cane molasses as the main carbon substrate.

Acknowledgements

This work was supported by a research grant from the

Universidad de Buenos Aires, Argentina (Project I038).

We are grateful to Dr Susana Passeron for critically

reviewing the manuscript. MFK and PIN are graduate fel-

lows of CONICET, and MAG is a CONICET researcher.

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Modelling the freezing response of baker’s yeast prestressed cells: a statistical approach

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