© WVU Biology 2019Learning Goals• Measure small volume.docx

© WVU Biology 2019
Learning Goals
• Measure small volumes accurately using micropipettes
• Perform dilution calculations
• Use a spectrophotometer to get absorbance values for
solutions
• Create standard curves by hand and with Excel
• Quantify target molecules with a standard curve
• Accurately and completely describe the methods you choose in
scientific journal style
By the end of lab today, you will be able to:
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PRACTICING LAB
SKILLS-JUST DO IT!
May 30
© WVU Biology 2019
Learning Goals
• Measure small volumes accurately using micropipettes
• Perform dilution calculations
• Use a spectrophotometer to get absorbance values for

solutions
• Create standard curves by hand and with Excel
• Quantify target molecules with a standard curve
• Accurately and completely describe the methods you choose in
scientific journal style
By the end of lab today, you will be able to:
© WVU Biology 2019
© WVU Biology 2019
Light
Source
Entrance
slit
Dispersion
Device
Exit slit
Cuvette with sample
Detector
Understanding the Spectrophotometer
(Turn on your spec now.)

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If you pass a beam of orange-red (625 nm) light through each
sample, which
would have the LOWEST absorption?
A B C D
Hint: Remember, if something looks green, that means it
REFLECTS green and
ABSORBS everything else.
From left to right, cuvettes contain increasing
concentrations of green dye.
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Getting comfortable w/ concentrations
1. Which grid has the highest concentration of dots?
2. What are the concentrations of the grids?
3. On Grid A, which box (small or large) has the highest
concentration of dots?
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Getting comfortable w/dilutions
= 0.9 ml of water

= 0.1 ml
Your new solution is ____
as concentrated as the
original solution.
Your new solution is ____
as dilute as the original
solution.
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Getting comfortable w/dilutions
• Turn to the “Making Dilutions” section (pg. 24) in your
manual and answer the
questions. Use the formula C1V1=C2V2 to help.
• Hint: You can use the formula C1V1 = C2V2 to solve this
problem.
• I) Your original solution had a concentration (C1) ____ g/L.
• II) Your new solution has a concentration (C2) of ____ g/L in
a volume of ____L (V2).
• III) You will need to add ____ L (V1) of your original stock
solution plus _____ L of water to make 100 ml of
the new solution.
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y = 0.2755x + 0.0562
R² = 0.9977

0
0.5
1
1.5
2
2.5
0 1 2 3 4 5 6 7 8
M
ea
n
Ab
so
rb
an
ce
a
t 6
25
nm
Concentration of Fast Green (ug/ml)
Standard Curve

Standard Curves
• Absorbance values increase with increasing concentrations of
dye.
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Standard Curves
• Today, you will be making a standard curve using methylene
blue dye.
• You will need to mix the correct amounts of water and stock
solution to create the
concentrations needed for your standard curve.
• Fill in the table on page 25 of your lab manual.
• Use can the formula C1V1=C2V2.
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How to use micropipettes
Link to video
http://www.sigmaaldrich.com/life-science/cell-culture/learning-
center/cell-culture-videos/the-micropipette.html
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Setting Micropipette Volumes

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Follow the directions in your manual to complete the
Micropipetting Practice Activity
Remember:
• Adjust the volume of the
micropipette using the volume
adjustment knob, not the plunger
button
• Depress the plunger button to the
first stop when aspiring liquids-not
the second
• Submerge the disposable tip just
below the surface of the liquid when
aspirating
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Standard Curves
• Set up tubes for you standard curve and measure the
absorbance values
using the instructions starting on pg. 25 of the lab manual.
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“Blanking” a Spectrophotometer
• Why is a “blank” used with a spectrophotometer?
• A blank is used to eliminate background absorbance from your
sample caused by the
cuvette and your reagents.
• What should be used as a “blank”?
• A blank should contain everything that you have have in your
sample except what you are
measuring.
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Estimating Unknown Concentrations
• Measure absorbance values for “unknowns”
• Use your hand-sketched standard curve (p. 27) to estimate the
concentration.
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Compare the curves. What could have gone wrong for
student 2?
0
1
2

3
4
5
6
0 1 2 3 4 5 6
A
bs
or
be
nc
e
60
0
nm
concentration (mg/mL)
0
1
2
3
4

5
6
7
0 1 2 3 4 5 6
A
bs
or
be
nc
e
60
0
nm
concentration (mg/mL)
Standard curve from Student 1 Standard curve from Student 2
© WVU Biology 2019
Calculating Unknown Concentrations
• Plot your standard curve in Excel
• Instructions are found in Appendix C of your lab manual.

• Use the equation of the line to calculate the concentrations of
your unknowns.
• Recall: y = mx + b
• y = absorbance value from spectrophotometer
• m = the slope of the line
• x = concentration of your unknown
• b = y-intercept
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CLEAN UP!!
• Dispose of solutions in the appropriate waste containers.
• Wash tubes thoroughly with water and place the tubes upside
down in the rack
to dry.
• Dispose of used tips, tubes, or kimwipes in the trash.
• Wipe down benches.
• Wash hands.
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SpeakWrite Approach
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Writing an
Introduction
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Writing an
Introduction
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Writing an
Introduction
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Using Scientific Literature Part B-Introduction
• Find a primary scientific article to be used as a source of
background
information for your Biofuels Introduction
• All groupmates should use a different primary article
• Paraphrase the article
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The Eberly Writing Studio
• Consider having someone read it over who is not in the

class
• The Eberly Writing Studio
• Dial 304-293-5788 to schedule an appointment or stop by G02
Colson Hall to see if a tutor is available.
• Appointments can also be made online
• http://speakwrite.wvu.edu/writing-studio
http://speakwrite.wvu.edu/writing-studio
© WVU Biology 2019
BIOTECHNOLOGICAL PRODUCTS AND PROCESS
ENGINEERING
Optimization of temperature, sugar concentration,
and inoculum size to maximize ethanol production
without significant decrease in yeast cell viability
Cecilia Laluce & João Olimpio Tognolli &
Karen Fernanda de Oliveira & Crisla Serra Souza &
Meline Rezende Morais
Received: 27 October 2008 /Revised: 16 January 2009
/Accepted: 19 January 2009 /Published online: 21 February
2009
# Springer-Verlag 2009
Abstract Aiming to obtain rapid fermentations with high
ethanol yields and a retention of high final viabilities

(responses), a 23 full-factorial central composite design
combined with response surface methodology was
employed using inoculum size, sucrose concentration, and
temperature as independent variables. From this statistical
treatment, two well-fitted regression equations having
coefficients significant at the 5% level were obtained to
predict the viability and ethanol production responses.
Three-dimensional response surfaces showed that increas-
ing temperatures had greater negative effects on viability
than on ethanol production. Increasing sucrose concentra-
tions improved both ethanol production and viability. The
interactions between the inoculum size and the sucrose
concentrations had no significant effect on viability. Thus,
the lowering of the process temperature is recommended in
order to minimize cell mortality and maintain high levels of
ethanol production when the temperature is on the increase
in the industrial reactor. Optimized conditions (200 g/l initial
sucrose, 40 g/l of dry cell mass, 30 °C) were experimentally
confirmed and the optimal responses are 80.8±2.0 g/l of
maximal ethanol plus a viability retention of 99.0±3.0% for
a 4-h fermentation period. During consecutive fermenta-
tions with cell reuse, the yeast cell viability has to be kept at
a high level in order to prevent the collapse of the process.
Keywords RSM . Viability. Ethanol production .
Temperature . Sugar concentration . Inoculum size
Introduction
High ethanol yields in a short fermentation time are an
economically relevant factor in industrial ethanol produc-
tion. However, this is dependent on the yeast strain, type of
process (batch or fed-batch), cell density, temperature, and
sugar concentration and enrichment of the medium with the

proper nutrients, along with other factors that influence the
microbial activity. Studies related to ethanol production
have been carried out in complex and synthetic media.
Although these have not yet being implemented on an
industrial scale due to economical reasons, a synthetic
medium exhibits favorable characteristics over the tradi-
tional complex or natural media since it is composed of
pure chemicals in precisely known proportions (Zhang and
Greasham 1999).
Appl Microbiol Biotechnol (2009) 83:627–637
DOI 10.1007/s00253-009-1885-z
C. Laluce (*) : M. R. Morais
Department of Biochemistry and Biotechnological Chemistry,
Instituto de Química de Araraquara-UNESP,
Caixa Postal 355,
14801-970 Araraquara, Sao Paulo, Brazil
e-mail: [email protected]
M. R. Morais
J. O. Tognolli
Department Analytical Chemistry,
Instituto de Química de Araraquara-UNESP,
Caixa Postal 355,
14801-970 Araraquara, Sao Paulo, Brazil
K. F. de Oliveira : C. S. Souza
Programa de Pós-Graduação Interunidades em Biotecnologia,
Institute of Biomedical Sciences,
Avenida Prof. Lineu Prestes, 1730-Edifício ICB-IV,
Sala 03-Cidade Universitária,
CEP: 05508-900 Sao Paulo, Sao Paulo, Brazil
K. F. de Oliveira

C. S. Souza
High sugar concentrations can inhibit both yeast growth
and fermentative activities. As described in literature
(Casey and Ingledew 1985), ethanol inhibition becomes
significant in the concentration range of 15–25% sugar
(w/v), while complete inhibition of the fermentation has
been reported at 40% glucose (w/v) in batch cultures
(Holcberg and Margalith 1981).
Typical yeast fermentations require temperatures be-
tween 30 and 35 °C to maximize ethanol production
(Damore et al. 1989). Yeast strains used for the commercial
production of ethanol usually produce lower levels of
ethanol at high temperatures. Concentrations of ethanol
above 3% (w/v) lead to decreases in the maximal
temperature of growth (Casey and Ingledew 1986). Strains
isolated from Brazilian alcohol plants have produced high
levels of ethanol in batch cultures operating within the
range of 35 to 40 °C in a rich medium containing sucrose,
yeast extract, and peptone (Laluce et al. 1991), but losses in
viability were greater at 40 °C.
Ethanol is well known as an inhibitor of microbial
growth. The rate of ethanol production and its accumulation
within cells of Saccharomyces cerevisiae in rapid fermen-
tations leads to sharp drops in viability (Dasari et al. 1990).
In addition, the loss in viability leads to decreases in the
activity of the alcohol dehydrogenase due to high levels of
internal ethanol (Nagodawithana et al. 1974). Rapid
fermentation also enhances thermal death (Loureiro and
van Uden 1986; Nagodawithana and Steinkraus 1976).
Nevertheless, some strains of S. cerevisiae show tolerance

to ethanol and can be adapted to high concentrations of
alcohol (Alexandre et al. 1994). A tolerant strain of yeast
isolated from a Brazilian alcohol plant was able to produce
ethanol in batch cultures having up to 8% (v/v) ethanol
added initially as described by Peres and Laluce (1998).
Any natural strain of S. cerevisiae is able to tolerate up to
14–16% (v/v) of ethanol excreted into the medium (Casey
et al. 1983) or even up to 21% (v/v), depending on the
nutritional supplementation (Thomas and Ingledew 1992).
Ethanol also induces cell lysis (Jones 1989) due to the
formation of cross-linked peptidoglycan, which is aggra-
vated by increasing the temperature to above 35 °C.
Statistical methods can either identify or quantify the
various interactions occurring between the independent
variables and the corresponding microbial responses.
Mathematical models generated by the statistical methods
allow the prediction of process responses such as ethanol
production and viability. In the present work, experimental
design and response surface methodology (RSM) were used
to study the effects of increasing inoculum size, sucrose
concentration, and temperatures on rapid fermentations
with small variations in viability during ethanol production
in synthetic medium (Thomas et al. 1998). Rapid fermen-
tation was defined as a fermentation in which the ethanol
level increases from zero to 9.5% (v/v) in 6-h or less
(Nagodawithana et al. 1974).
Materials and methods
Microorganism
The hybrid strain 63M used in this study was constructed
using genetic segregants derived from industrial isolates of
S. cerevisiae (Laluce et al. 2002). This yeast was able to

grow overnight on yeast–peptone–dextrose (YPD) plates at
40 °C (Souza et al. 2007). A stock culture was stored at
4 °C on solid YPD medium with transfers to fresh medium
every 4 months.
Inoculum propagation
For the inoculum propagation, the synthetic medium
described by Thomas et al. (1998) was modified by
replacing the glucose with sucrose (carbon source) and by
adding 2% yeast extract to improve cell proliferation.
Separate solutions containing salts, trace elements, vitamins,
growth, and survival factors were prepared as described
by the authors (Thomas et al. 1998) and then mixed to give
the concentration of each ingredient as required for the
final medium: (NH4)2SO4, K2HPO4, KH2PO4, MgSO4,
CaCl2, NaCl, ZnSO4, H3BO3, KI, MnSO4, CuSO4,
Na2MoO4, CoCl2, FeCl3, biotin, calcium pantothenate,
folic acid, myo-inositol, niacin, p-aminobenzoic acid,
pyridoxine hydrochloride, riboflavin, thiamine hydrochlo-
ride, ergosterol, and Tween 80. The propagation was carried
out in 250-ml Erlenmeyer flasks, initially containing 50 ml
of synthetic medium (Thomas et al. 1998), which were
inoculated with fresh culture to start the propagation with
an initial cell density of 0.85 g/l. After 16-h propagation at
30 °C in a rotary shaker operating at 125 rpm, cells were
harvested by centrifugation at 5,000×g for 2 min at 4 °C.
The harvested cells were resuspended in sterilized water
and the washed cell pellet was separated by centrifugation.
In the next step, the washed cells were again resuspended in
sterilized water, resulting in a highly concentrated yeast
cream (160 g/l in dry weight or 48%, v/v), which was used
to start the high cell density fermentations.
Fermentation procedures

The synthetic medium (Thomas et al. 1998) containing
ammonium sulfate (nitrogen source) and other ingredients
as described above, except glucose (carbon source replaced
with sucrose), was used to study the effects of the
independent variables (sucrose, temperature, and inoculum)
on viability and ethanol formation during the fermentation.
628 Appl Microbiol Biotechnol (2009) 83:627–637
Yeast extract was not added to this medium. A solution
twice as concentrated (50 ml) containing the different
medium ingredients was prepared and sterilized as de-
scribed by the authors (Thomas et al. 1998) and then mixed
before adding different amounts of sucrose or inoculum as
follows: sucrose concentration varying from 100 to 200 g/l
in the final medium, inoculum amounts varying from
30 g/l (around 9%, v/v) to 40 g/l (dry weight), and
fermentation temperature varying from 30 to 40 °C. The
pH of the medium was also adjusted to 4.5 prior to
inoculation. Erlenmeyer flasks were sealed closed using
perforated rubber stoppers to which a glass tube was
inserted to allow the fermentation gas to escape from the
100 ml of final medium. A second glass tube was inserted
to the rubber stopper to collect samples from the bottom of
the Erlenmeyer flasks during the fermentation. The flasks
were then transferred to a rotary shaker operating at
100 rpm for the duration of the fermentation period.
Analytical assays
The determinations were cell viability using the methylene-
blue method (Lee et al. 1981); total reducing sugar in acid
hydrolysates (1.2 M HCl for 10 min at 60 °C), using the
3,5-dinitrosalicylic acid method (Miller 1959); and ethanol

concentration using a gas chromatograph (model CG-37;
Instrumentos Científicos, São Paulo, Brazil). For the
biomass assays, cells were washed by vacuum filtration
and dried at 105 °C until constant weight and expressed as
grams per liter in the final medium.
Experimental design
The RSM is a technique (Box and Wilson 1951) that
consists of the following: (1) the designing of experiments
that will yield adequate and reliable measurements of the
response of interest, (2) the determining of a mathematical
model that best fits the data obtained from the design, and
(3) the determining of optimal values for the experimental
factors that will give maximal or minimal values for the
responses. In the present study, the dependent variables
were ethanol production (Y1) and viability (Y2), which were
assayed after 4-h fermentation periods. The independent
variables were temperature (X1), sucrose concentration (X2),
and inoculum size (X3), as shown in Table 1. This table also
shows that 35 °C, 150 g/l sucrose, and 35 g/l inoculum
were adopted as central points to predict the dependent
variables. A 23 full-central-composite design with replica-
tion at the central point and having six axial points (n=6)
was used for the optimization, with the data being obtained
from a total of 20 experiments carried out in triplicate. Data
analysis was performed using the MINITAB statistical
software package (version 14.0) with a level of significance
of 5% and a confidence level of 95% (p=0.05).
Statistical analysis of data by RSM
RSM is a sequential procedure with the initial aim of
allowing the researcher to rapidly and efficiently obtain
data near optimum values. It includes a full factorial central

composite design and regression analysis. In the present
work, response surface models were fitted to the ethanol
production and viability using the MINITAB software
package (version 14.0). The experimental results of the
RSM were fitted via the response surface regression
procedure using the following second-order polynomial
equation:
Yi ¼ b0 þ b1X1 þ b2X2 þ b3X3 þ b11X 21 þ b22X 22
þ b33X 23 þ b12X1X2 þ b23X2X3 þ b13X1X3; ð1Þ
where the following can be found: Yi is the predicted
response; X1, X2, and X3 are the independent variables; bo is
the intercept term; b1, b2, and b3 are the linear effects; b11,
b22, and b23 the square effects; and b12, b23, and b13 are the
interaction terms. Y represents viability (Y1, percent) or
ethanol production (Y2, grams per liter), while X1 (temper-
ature), X2 (sucrose concentration), and X3 (inoculum
concentration) were independent values. This equation
represents an empirical model, in which the response
functions allow the estimation of responses due to changes
in the dependent variables. This model was regressed given
two-fitted model equations, one for viability response
(Eq. 2) and the other for the ethanol production (Eq. 3) as
described in Table 3 (“Results” section).
Independent variables Symbols Range of natural levels
−1.682 −1.000 0.000 +1.000 +1.682
Temperature (°C) X1 30 32 35 38 40
Initial sucrose concentration (g/l) X2 100 120 150 180 200
Yeast inoculum sizes (g/l) X3 30 32 35 38 40

Table 1 Levels of real and
codified values of independent
variables utilized in the 23 full
central composite design
Appl Microbiol Biotechnol (2009) 83:627–637 629
Application variance analysis to the fitted models
The adequacy of the fitted model equations was evaluated by
application variance analysis (ANOVA), using the MINITAB
software package (version 14). If the model is not satisfactory,
a more complex model with a better fit is required, and this is
indicated by the analysis of variance. In this work, the F test
for regression was taken as significant at a significance level
of 5% or a 95% confidence level (p=0.05) for both ethanol
production and viability responses. If the F test is significant
for its lack of fit, then a more complicated model is needed.
Both the t test (measuring how large the coefficient is in
relation to its standard error) and p values (reflecting the
chance of getting a larger t value and also indicating the
patterns of the interaction among the variables) were used to
confirm the significance factor of the model equations.
Surface and contour plots
The response surface was plotted to understand the inter-
actions between variables and to determine each variable’s
optimum response level. In the present work, surface and
contour plots of key variables were derived from linear (plain
surface graphs) and quadratic (curved surfaces) models,
fitting experimental data to calculate optimal responses for
ethanol production and viability. The plots were obtained
with the aid of the STATISTICA software package (version

7.0). The simultaneous interactive effects of the independent
variables are shown by the three figures described in the
“Results” section.
Optimization of response and model validation
The “Response Optimizer” option of the MINITAB software
package (version 14) was used to search for a combination of
the factors involved that jointly optimize ethanol production
with the retention of a high viability. The range of viability
used for optimization was between 80% and 100% and for
ethanol between 68 and 100 g/l. Desirability is a measure of
how well the optimal solution satisfies the aim of the
responses. A desirability of one indicates complete satisfac-
tion, while a desirability of zero indicates that the response is
not acceptable. In order to validate the optimized conditions
(40 g/l biomass in dry weight, 30 °C, 200 g/l sucrose) derived
from the use of the “Response Optimizer”, experiments were
carried out in triplicate to obtain time curves for viability,
ethanol production, biomass, and total residual sugar.
Results
Using factorial design and RSM, variations in viability and
ethanol production were predicted as functions of the
variations in inoculum size, sucrose concentration, and
temperatures.
Factorial planning
Table 2 shows the predicted and experimental data related
to both the ethanol production and the viability responses,
which were obtained using a factorial design. Twenty
experiments were carried out in 4-h fermentation periods
using different combinations of the independent variables.

The highest predicted values of ethanol (77.2 g/l) and
viability (87.5%) were observed in run 10, in which real
values of viability and ethanol were 87.2% and 77.0 g/l,
respectively. High real values of viability (around 92%) were
also observed in runs 14 and 19, but the real levels of ethanol
were much lower (54.6 g/l in run 14 and 68.7 g/l in run 19).
Real and predicted data obtained can be low, as follows:
65.9% viability and 55.2 g/l ethanol in run 7 and 59.1%
viability and 60.1 g/l ethanol in run 8, as shown in Table 2.
High temperatures (38 °C in run 7 and 40 °C in run 8, as
shown in Table 2) inhibited the ethanol production and
killed the cells, as is indicated by the low values of viability.
Model fitting using RSM
Using the data shown in Table 2, the proposed polynomial
experimental model (Eq. 1, “Materials and methods”) was
regressed, resulting in two expanded equations or fitted
models, which are shown in Table 3, and these exhibit
maximal viability (Eq. 2 or Y1 model) and ethanol
production (Eq. 3 or Y2 model). The R-squared value (R
2,
coefficient of correlation resulting from the regression of
the model equation) provides a way to evaluate how much
the measured variability could be explained by the
experimental factors and their interactions in the observed
responses. The matching quality of data, provided by the
model equations (Eq. 2 for viability and Eq. 3 for ethanol
production as shown in Table 3), indicates that 98.7% of the
variability (R2=0.987) in the viability response and 98.6%
(R2=0.986) in the ethanol production response can be
explained by the models. Regression also provides a way to
evaluate the nature and the degree of correlation between
dependent and independent variables. The closer the R2

value is to 1.00, the stronger the model and the better the
response predictions (Haaland 1989). The R2 value is
always between zero and one. For the ethanol production
model, a R2=0.986 was obtained, indicating the adequacy
(or a high probability) of this model. The adjusted R2 (adj.
R2), which was derived from the sample size and from the
number of terms in the model equation, corrects the
predicted R2 value. In the present case, the differences
between values of R2 and adj. R2 are small, and thus, they
are in reasonable agreement.
Positive signs in terms of the fitted equations (Eqs. 2 and
3 in Table 3) represent synergistic effects, while negative
signs indicate antagonistic effects. Interactive, linear, and
squared effects can be observed among independent
variables. The linear equation model (Eq. 2, Table 3) shows
the linear and negative effects of the temperature (X1) and
inoculum (X3), as well as the positive effect of the
interaction between temperature (X1) and inoculum size
(X3). Effects of independent variables on ethanol production
are related to a greater number of terms, as shown in Eq. 3
(Table 3). This equation shows the linear and positive
effects of sucrose concentration (X2) and temperature (X1)
on ethanol production. In addition, Eq. 3 (Table 3) shows a
negative interaction between temperature (X1) and inocu-
lum size (X3), but a positive interaction between sucrose
concentration (X2) and inoculum size (X3) are also shown.
Negative and quadratic effects of temperature X 21
� �
and

sucrose concentration X 22
� �
can also be observed in Eq. 3
(Table 3).
Analysis of variance (ANOVA) for the fitted models
The two fitted equations (Table 3), resulting from the
analysis of variance, were a linear equation (Eq. 2) for
viability and a quadratic equation (Eq. 3) for ethanol
production. Table 4 describes the F values (statistical
significance of the model) and p values for the viability
(Eq. 2) and the ethanol production (Eq. 3) models. The
regression F values were high enough to indicate statistical
significance and that most of the variations in the response
variables can be explained by the regression equations.
Concerning the R2 value (Table 3), both Eqs. 2 and 3 were
highly significant and adequate to represent the true
relationship between the three independent variables.
Table 3 Best-fit equations for viability and ethanol production
responses resulting from the complete 23 factorial design
Responses Best-fit equations Regression R2 adj. R2
Viability (Y1) Y1 ¼ 509:379 � 10:00X1 � 10:723X3 þ
0:172X1:X3 Linear 0.987 0.975
Etanol (Y2) Y2 ¼ �481:391 þ 23:050X1 þ 0:798X2 � 0:229X
21 � 0:003X 22 � 0:187X1:X3 þ 0:017X2:X3 Quadratic 0.986
0.974
X1 temperature, X2 initial sucrose concentration, X3 inoculum,
F Fisher test for regression, R
2 coefficient of determination, adj. R2 adjusted R2

Table 2 Experimental and predicted values of ethanol and
viability resulting from the application of the 23 full-central-
composite design
Runs Independent variables (real values) Yeast cell viability
(%) Ethanol (g/l)
Temperature (°C) Sucrose (g/l) Inoculum (g/l) Experimental
Predicted Experimental Predicted
1 35 200 35 79.9 79.8 73.6 73.6
2 35 150 30 79.6 81.0 64.4 65.5
3 35 100 35 75.8 77.3 45.8 45.6
4 38 180 32 70.5 69.6 68.7 68.1
5 35 150 35 78.4 78.9 67.8 67.2
6 30 150 35 96.3 96.3 62.8 63.4
7 38 120 32 65.9 64.6 55.2 55.5
8 40 150 35 59.1 60.4 60.1 59.3
9 35 150 40 81.0 81.0 70.7 69.4
10 32 180 38 87.2 87.5 77.2 77.0
11 38 120 38 69.9 69.0 51.0 51.5
12 35 150 35 80.1 78.9 69.0 67.2
13 35 150 35 77.4 78.9 65.7 67.2

14 32 120 32 92.2 91.2 54.6 53.6
15 35 150 35 77.1 78.9 65.7 67.2
16 32 120 38 89.5 89.3 55.6 56.3
17 35 150 35 80.5 78.9 67.7 67.2
18 35 150 35 80.1 78.9 67.3 67.2
19 32 180 32 92.0 91.9 68.7 68.3
20 35 180 38 71.4 71.4 68.9 70.1
Concerning viability (Eq. 2), the regression F test was
highly significant (p=0.000), as shown in Table 4. As the
significance of the regression F test shows a value of 5%
(p=0.05), the F test was significant for the linear regression
(p<0.05), but not for the lack of fit and the square (p>0.05)
of the linear model for viability (Table 4). However, the
value of adj. R2=0.975 obtained for the viability indicates a
well-fitted model, as shown in Table 3.
Concerning ethanol production (Eq. 3 in Table 3), the F
test for the regression (Table 4) was also highly significant
(p=0.000). As the significance of the regression F test
shows a level of 5% (p=0.05), the F test was significant for
both the linear and square regressions (p<0.05), but not for
the lack of fit (p>0.05). Thus, a well-fitted model was
obtained for the equation of ethanol production, as
indicated by the adj. R2 value of 0.974 shown in Table 3.
In addition, the value of adj. R2=0.974 for ethanol

production also indicates a well-fitted model in Table 3.
Table 5 shows the regression coefficients, standard errors
of coefficients, t values, and p values for the models
representing viability (Eq. 2) and ethanol production
(Eq. 3). Concerning viability, Table 5 shows that the linear
effects of temperature (X1) and inoculum size (X3) on
viability (Eq. 2) were negative and significant at a 5%
probability level (p value<0.05). The linear effect of
sucrose concentration (X2) on viability was negative but
not significant (p>0.05). Concerning interactions between
variables, positive but not significant interactions (p value>
0.05) were noted between temperature (X1) and sucrose
concentration (X2, p>0.05), so the coefficient X1.X2 was
omitted from Eq. 2 as described in Table 3 for viability. On
the other hand, the interactions between sucrose concentra-
tion (X2) and inoculum size (X3) were negative but not
significant, so that X2.X3 was also omitted from Eq. 2.
In relation to ethanol production, Table 5 shows that the
quadratic effects (Eq. 3 in Table 3) of temperature (X1) and
sucrose concentration (X2) were significant (p<0.05), while
the effect of inoculum size (X3) was positive, although not
significant (p>0.05). Positive and significant interactions
were observed between sucrose concentration (X2) and
inoculum size (X3), while a negative but significant
interaction was observed between temperature (X1) and
inoculum size (X3). Despite the negative coefficient, the
interactive effects between temperature (X1) and sucrose
concentration (X2) were not significant (p value>0.05), as
shown in Table 5.
Three-dimensional surface and contour plots for ethanol
production and viability

Figures 1, 2, and 3 show the three-dimensional response
surfaces resulting from the fitted equations to investigate
Table 4 Analysis of variance (ANOVA) for the linear model of
viability and the squared model of ethanol production using
strain 63M of S.
cerevisiae and 4-h fermentation periods
Responses Sources of
variations
Seq sum
of squares
Adj sum
of squares
Degrees
of freedom
Adj mean
of squares
F ratio p Values
Viability (fitted Eq. 2)
Regression 1,604.98 1,604.98 9 178.33 83.60 0.000
Linear 1,563.83 25.03 3 8.34 3.91 0.044
Square 9.30 9.30 3 3.10 1.45 0.285
Interaction 31.84 31.85 3 10.62 4.98 0.023
Residual error 21.33 21.33 10 2.13 – –

Lack-of-fit 10.16 10.16 5 2.03 0.91 0.540
Pure error 11.17 11.17 5 2.24 – –
Total model 1,626.31 – 19 – – –
Ethanol (fitted Eq. 3)
Regression 1,185.92 1,185.92 9 131.77 80.43 0.000
Linear 988.78 91.79 3 30.60 18.68 0.000
Square 154.33 154.33 3 51.44 31.40 0.000
Interaction 42.82 42.82 3 14.27 8.71 0.004
Residual error 16.38 16.38 10 1.64 – –
Lack-of-fit 7.85 7.85 5 1.57 0.92 0.535
Pure error 8.53 8.53 5 1.71 – –
Total model 1,202.31 – 19 – –
Seq sequential, Adj adjusted
the interactions between variables and to determine the
optimal values of each factor for maximal retention of
viability (linear model in Eq. 2) and ethanol production
(quadratic model in Eq. 3).

The interactive effects between temperatures (X1) and
sucrose concentration (X2) on viability and ethanol produc-
tion, using 35 g/l inoculum as the central point, are shown
in Fig. 1. The two response surface graphs show that
increases in temperature had greater negative effects on
viability (Fig. 1b) than on ethanol production (Fig. 1a).
Increasing sucrose concentrations (X2) improved ethanol
production (Fig. 1a) with small effects on viability
(Fig. 1b). The best value of viability (Fig. 1b) was 90% at
32 °C, while the corresponding value of ethanol produced
was around 74 g/l. At 30 °C, the amount of ethanol
produced was lower.
The interactive effects between temperatures (X1) and
inoculum size (X3) are shown in Fig. 2, using 150 g/l
sucrose as the central point. The two response surface
graphs show that the increases in temperature (X1) had a
greater negative effect on viability (Fig. 2b) than on ethanol
production (Fig. 2a). It was observed that increasing the
inoculum size (X3) improved ethanol production (Fig. 2a)
with little or no effect on viability (Fig. 2b). The maximal
value of ethanol was seen to around 32 °C (around 73 g/l
ethanol), while the corresponding viability was 90%. The
maximal viability was obtained with 40 g/l inoculum at
30 °C, but the ethanol accumulated was lower. Thus,
increases in temperature improved ethanol production
(Fig. 2a) up to a threshold temperature of 32 °C, and then
Fig. 1 Response surface curves and contour plot lines showing
the
variations in ethanol production (a quadratic model) and
viability (b
linear model) as functions of the interactive effects between
temperature (X1) and sucrose concentration (X2) when 35 g/l
inoculum

is used as the central point
Table 5 Regression coefficients, standard errors, t test, and
signifi-
cance level for the models representing ethanol production
responses
and viability responses as a function of variations in the
independent
variables in 23 full-central-composite design
Terms of model
equations
Regression
coefficient
Standard
error
t Test p Value
Viability
Constant 509.379 114.244 4.459 0.001
X1 (temperature) −10.000 3.707 −2.697 0.022
X2 (sucrose) −0.098 0.312 −0.315 0.759
X3 (inoculum) −10.723 3.707 −2.892 0.016
X1.X1 −0.020 0.043 −0.460 0.655
X2.X2 −0.000 0.000 −0.322 0.754
X3.X3 0.082 0.043 1.929 0.083
X1.X2 0.012 0.006 2.082 0.064
X1.X3 0.172 0.057 3.002 0.013

X2.X3 −0.007 0.006 −1.259 0.237
Ethanol production
Constant −481.391 100.116 −4.808 0.001
X1 (temperature) 23.050 3.249 7.094 0.000
X2 (sucrose) 0.798 0.273 2.918 0.015
X3 (inoculum) 3.709 3.249 1.142 0.280
X1.X1 −0.229 0.037 −6.112 0.000
X2.X2 −0.003 0.000 −7.947 0.000
X3.X3 0.010 0.037 0.280 0.785
X1.X2 −0006 0.005 −1.182 0.264
X1.X3 −0.187 0.050 −3.718 0.004
X2.X3 0.017 0.005 3.304 0.008
Significance at 5% probability level; R2 of 0.987 for ethanol
production and 0.986 for viability; R2 adjusted for ethanol
production
was 97.4% and 94.5% for viability
Coef. coefficients
ethanol levels decreased above this temperature showing
negative effects on viability (Fig. 2b). The viability
(Fig. 2b) did not increase with the inoculum size (X3).
The interactive effects between amounts of sucrose (X2)
and inoculum (X3) are shown in Fig. 3, using 35 °C as the
central point. The two response surface graphs show that

increases in sucrose concentration (X2) had a greater
positive effect on ethanol production (Fig. 3a) than on
viability (Fig. 3b). In addition, sucrose concentration (X2)
and inoculum size (X3) did not have impacting effects on
viability (Fig. 3b), as is also shown in Fig. 2. The maximal
value of ethanol produced was around 80 g/l, while the
corresponding viability was around 80%.
Optimization and experimental validation of the models
The determination of the optimal values of the factors that
affected the ethanol production and viability (dependent
variables) was attempted using the Response Optimizer of
the MINITAB software package (version 14). Based on the
Response Optimizer, the predicted optimal fermentation con-
ditions were as follows (Table 6): 200 g/l sucrose, 30 °C, and
40 g/l inoculum.
The experimental validation of these three optimal con-
ditions (predicted values) was carried out in shaken flasks, and
the corresponding time curves of the fermentation process are
shown in Fig. 4. The experimental responses (Fig. 4 and
Table 6) were: 99.0±3.0% viability and 80.8±2.0 g/l ethanol
the
viability (b
linear model) as functions of the interactive effects between the
inoculum size (X3) and sucrose concentration (X2) when 35 °C
is used
as the central point
the

viability (b
linear model) as functions of the interactive effects between
sucrose
concentration (X2) and inoculum sizes (X3) when 150 g/l
sucrose is
used as the central point
in a 4-h fermentation. In a 7-h fermentation, the responses
were (Fig. 4): 102.1±4.0 g/l ethanol and 95.0±2.2% viability.
The initial biomass (Fig. 4) was 42.4±2.0 g/l and the final
biomasses were 43.4±2.2 g/l in 4 h and 44.5±2.8 g/l in 7 h of
fermentation.
Discussion
An economically relevant factor associated with industrial
ethanol production is to obtain high ethanol yields over a
succession of fast fermentation cycles, in which cells from
one cycle are used as inoculum of the next fermentation
cycle. The retention of high viabilities during the fermen-
tation cycles is a prerequisite to carry out a long-lasting
succession of fermentation cycles. In the present study,
short fermentation times were obtained by using high
amounts of inoculum to start simple batch fermentations
at high cell density. However, conditions for growth and
metabolism at high cell densities are less favorable due to
hindered access to nutrients, space limitations, and cell
interactions (Jarzebski et al. 1989). In addition, variation in
temperature often occurs in the summertime due to
fluctuations in the temperature of the cooling water of the
bioreactors mainly in tropical climates. To this end, the
optimization of independent variables (temperature, sucrose

concentration, and inoculum size) and the corresponding
responses (ethanol produced and viability retention) were
obtained in the present work using a 23 full-central-
composite design.
A factorial design (Table 2) involving predicted and
experimental data indicated that the variations in viability at
the end of the fermentation did not necessarily reflect the
variations in ethanol production. High levels of viability
were obtained, but the corresponding levels of ethanol can
be much lower than expected. This was due to the kind of
interactive effects between the independent variable. In
literature (Dasari et al. 1990), increases in viability have
been described as related to decreases in ethanol yields due
to the inhibitory effects of ethanol.
In the present work, two fitted equations (Eqs. 2 and 3)
were obtained, and they were highly significant and
adequate to represent the true relationship between the
Fig. 4 Kinetics of growth and fermentation by strain 63M under
the
best conditions as indicated by the MINITAB’s Response
Optimizer
(software package, version 14): biomass (circles); viability
(squares);
ethanol (upside-down triangles); total reducing sugar or TRS
(right-
side-up triangles)
Parameters Goal Lower Target Upper Weight
Viability (%) Maximal 80 90 100 1
Ethanol (g/l) Maximal 68 83 100 1

Global solution
Temperature=30 °C
[Sucrose]=200 g/l
[Inoculum]=40 g/l
Predicted responses
Viabillity=90.00%, desirability=1.000
Ethanol=82.65 g/l, desirability=0.9770, composite
desirability=0.9884
Experimental responses
Viability=99.0±5.4%
Ethanol=80.8±4.3 g/l
Table 6 Response optimization
using the Response Optimizer
of the MINITAB software
package (version 14)
three independent variables, as shown by the R2 values, as
shown for ethanol production and viability. The interactive
effects between the process variables can be synergistic or
antagonistic, as shown by the regression coefficients
(Table 5) and responses surface graphs.

Concerning the interaction between ethanol accumula-
tion and inoculum size, the response surface graph showed
that increasing ethanol yields can be obtained using
increasing inoculum sizes. As also described in literature
(D’Amore et al. 1989), the rate and level of ethanol
produced increased with the increases in inoculum sizes.
In addition, Vega et al. (1987) proposed a mathematical
model, which showed that increasing amounts of inoculum
decreased the severity of ethanol inhibition. However, a high
level of inoculum leads to rapid fermentation that may not
always be favorable to the process depending on the strain
and levels of ethanol produced. In the present work, the
interaction between sugar concentration and inoculum sizes
led to increases in the levels of the ethanol produced due to a
strong and positive interaction between sugar concentration
and inoculum size in high-cell-density cultures.
As observed in this work, interactions between sucrose
concentration and inoculum size did not affect the final
viability of the process in high-cell-density cultures.
However, growth inhibition and cell mortality can be
observed due to the increases in ethanol toxicity in media
with high sugar concentration, and this toxicity is aggra-
vated at high temperatures (Grubb and Mawson 1993;
present work). In addition, correlations between the drops
in viability and a massive leakage of intracellular metabo-
lites, which were particularly severe above 10% ethanol in
the media, have been described (Cot et al. 2007). If
consecutive and/or abrupt drops in viability frequently
occur, the fate of the fermentation process is its own
collapse. When this happens, the entire process has to be
restarted, incurring great economic losses for distilleries.
The experimental assays confirmed the reliability of the
two mathematical models proposed to predict the variations
in viability and ethanol production during fermentation in

the present work. Despite the very low biomass accumu-
lated at the end of the fermentation processes in the
validation experiments (Fig. 4), the value of the final
viability was kept at a high value (95.0±2.2%) for the 7-h
fermentation, indicating that the cell population was alive
in a quasistationary phase. A succession of fermentation
cycles with cell reuse (cells from one cycle used to
inoculate the next cycle) is not possible when significant
drops in cell mass or viability are observed during the
fermentation cycles. Nevertheless, a succession of fermen-
tation cycles can be carried out over months at the
industrial scale when cell proliferation is observed, and
this indicates that the yeast cell population has been kept
robust and healthy during the fermentation processes.
Values of optimized responses (viability and ethanol
production) depend on the type of process (batch or
continuous process), yeast strain, and other independent
variables (nutrients and their concentrations, aeration,
agitation, and so on), and these were not involved in this
optimization study. Using a batch culture, kinetics of
ethanol production from molasses was optimized by other
authors (Rivera et al. 2006), showing that the maximal
value of biomass (Xmax) was obtained at 28 °C and Pmax
(ethanol) at 31 °C. In the present work, the experimental
validation of the statistical data carried out in batch cultures
(Fig. 4) showed a maximal ethanol production (102.1±
4.0 g/l) from 200 g/l sucrose after 7 h of fermentation at
30 °C without drops in viability (Fig. 4), but variations in
biomass were not observed due to the high number of cells
used as inoculum.
As indicated in the present work, it is seems feasible to
predict the effects of sugar concentration, temperature, and
inoculum size on viability and ethanol production for the
operation of alcohol plants using statistical methodologies.

When the sugar concentration increases, the process
temperature has to be decreased in order to prevent drops
in viability due to the ethanol-induced lethality of increas-
ing amounts of ethanol produced by the yeast cells. Thus,
the lowering of the process temperature is recommended in
order to minimize cell mortality and maintain high levels of
ethanol production when the temperature is increasing in
the industrial reactor.
The batch cultures used in the present work have some
limitations, mainly related to high sugar concentrations and
ethanol yields. To overcome these problems, the fed-batch
culture techniques have often been employed. In a pulse
fed-batch culture using the same yeast strain as was used in
the present work, high ethanol yields were obtained without
significant variations in viability at temperatures as high as
37 °C (Souza et al. 2007).
Acknowledgments We are grateful to FAPESP for research
grant
no. 2005/02840-0.
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Wang et al. Biotechnol Biofuels (2019) 12:59
https://doi.org/10.1186/s13068-019-1398-7
R E S E A R C H
QTL analysis reveals genomic variants linked
to high-temperature fermentation performance
in the industrial yeast
Zhen Wang1,2†, Qi Qi1,2†, Yuping Lin1*, Yufeng Guo1,
Yanfang Liu1,2 and Qinhong Wang1*
Abstract
Background: High-temperature fermentation is desirable for the
industrial production of ethanol, which requires
thermotolerant yeast strains. However, yeast thermotolerance is
a complicated quantitative trait. The understanding
of genetic basis behind high-temperature fermentation
performance is still limited. Quantitative trait locus (QTL) map-
ping by pooled-segregant whole genome sequencing has been
proved to be a powerful and reliable approach to
identify the loci, genes and single nucleotide polymorphism
(SNP) variants linked to quantitative traits of yeast.
Results: One superior thermotolerant industrial strain and one
inferior thermosensitive natural strain with distinct
high-temperature fermentation performances were screened
from 124 Saccharomyces cerevisiae strains as parent
strains for crossing and segregant isolation. Based on QTL
mapping by pooled-segregant whole genome sequencing
as well as the subsequent reciprocal hemizygosity analysis
(RHA) and allele replacement analysis, we identified and
validated total eight causative genes in four QTLs that linked to
high-temperature fermentation of yeast. Interestingly,
loss of heterozygosity in five of the eight causative genes
including RXT2, ECM24, CSC1, IRA2 and AVO1 exhibited
posi-

tive effects on high-temperature fermentation. Principal
component analysis (PCA) of high-temperature fermentation
data from all the RHA and allele replacement strains of those
eight genes distinguished three superior parent alleles
including VPS34, VID24 and DAP1 to be greatly beneficial to
high-temperature fermentation in contrast to their inferior
parent alleles. Strikingly, physiological impacts of the superior
parent alleles of VPS34, VID24 and DAP1 converged on
cell membrane by increasing trehalose accumulation or reducing
membrane fluidity.
Conclusions: This work revealed eight novel causative genes
and SNP variants closely associated with high-temper-
ature fermentation performance. Among these genes, VPS34 and
DAP1 would be good targets for improving high-
temperature fermentation of the industrial yeast. It also showed
that loss of heterozygosity of causative genes could
contribute to the improvement of high-temperature fermentation
capacities. Our findings would provide guides to
develop more robust and thermotolerant strains for the
industrial production of ethanol.
Keywords: High-temperature fermentation (HTF), Pooled-
segregant whole-genome sequence analysis, QTL
mapping, Reciprocal hemizygosity analysis, Allele replacement,
Saccharomyces cerevisiae
© The Author(s) 2019. This article is distributed under the
terms of the Creative Commons Attribution 4.0 International
License
(http://creat iveco mmons .org/licen ses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s)
and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The Creative Commons
Public Domain Dedication waiver (http://creat iveco mmons

.org/
publi cdoma in/zero/1.0/) applies to the data made available in
this article, unless otherwise stated.
Open Access
Biotechnology for Biofuels
*Correspondence: [email protected]; [email protected]
†Zhen Wang and Qi Qi contributed equally to this work
1 CAS Key Laboratory of Systems Microbial Biotechnology,
Tianjin Institute
of Industrial Biotechnology, Chinese Academy of Sciences,
Tianjin 300308,
China
Full list of author information is available at the end of the
article
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Page 2 of 18Wang et al. Biotechnol Biofuels (2019)
12:59
Background
Saccharomyces cerevisiae has been widely used for the
production of various fuels and chemicals, more recently,
eco-friendly bioethanol [1, 2]. Although robust indus-
trial S. cerevisiae strains produce ethanol from agricul-
tural wastes with high yield and productivity, the urgent
demand of larger production and minimum costs is still
challenging. Improved thermotolerance performance

can address this obstacle to some extent, since high-tem-
perature fermentation can greatly reduce cooling costs,
increase cell growth, viability and ethanol productivity
via facilitating the synchronization of saccharification
and fermentation [3, 4]. However, thermotolerance is a
complex quantitative trait and determined by a compli-
cated mechanism referring to the interaction of many
genes [5]. Thus, it is very challenging to develop robust
S. cerevisiae strains with enhanced thermotolerance to
meet industrial requirement.
Many efforts have been made to understand the molec-
ular mechanisms and genetic determinants underlying
yeast thermotolerance, but most of them focused on
laboratory strains, which display much lower thermal
tolerance than the robust industrial and natural yeast
strains [6]. Previous study indicated that industrial yeast
has evolved complex but subtle mechanisms to protect
the organism from high-temperature lesion by activat-
ing and regulating of specific thermal tolerance-related
genes to synthesize specific compounds [7]. To identify
novel genes and elucidate the intricate mechanism of
thermotolerance, many methods were developed [8–12].
Although these approaches have disclosed a number
of causative genes and revealed some compounds, e.g.
sterol composition, for responding to the thermal stress,
identification of quantitative trait genes still faced with
tremendous challenges, including variable contributions
of quantitative trait loci (QTL), epistasis [13], genetic
heterogeneity [14], etc.
With the rapidly development of high-throughput
genome sequencing, pooled-segregant whole genome
sequencing technology has been developed for efficiently
mapping QTLs related to complex traits [15, 16]. Sub-
sequent genetic approaches, such as reciprocal hemizy-

gosity analysis (RHA) and allele replacement analysis,
accelerated identification of the causative genes linked
to superior phenotypes [17]. S. cerevisiae as a model
organism is renowned for the acquisition of abundant
genetic markers [18], the ease of introduction of precise
genetic modification and the convenience of perform-
ing experimental crosses [19], thus perfectly suitable for
the application of QTL methodology to disclose complex
traits. The efficient methodology has facilitated identifi-
cation of several genomic regions and causative genes
related to the complex traits in S. cerevisiae, including
thermotolerance, ethanol tolerance, glycerol yield, etc. [5,
20–22]. However, up to now, the underlying molecular
mechanisms of thermotolerance in S. cerevisiae are still
unclear, and the identification of novel causative genes
continues to be of interest to accelerate the breeding of
robust yeast strains with improved high-temperature fer-
mentation performance.
In this study, to uncover genetic determinants linked
to high-temperature fermentation performance of the
industrial yeast, QTL mapping by pooled-segregant
whole genome sequence analysis and subsequent valida-
tion by RHA and allele replacement analysis were per-
formed. The scheme of this work was shown in Fig. 1.
Total eight genes containing nonsynonymous SNP vari-
ants in two major QTLs linked to the superior parent and
two minor QTLs linked to the inferior parent were iden-
tified and validated to be causative genes tightly associ-
ated with thermotolerance. Among these genes, loss of
heterozygosity in RXT2, ECM24, CSC1, IRA2 and AVO1
seemed to play beneficial roles in developing thermotol-
erance; meanwhile, the superior parent alleles of VPS34,
VID24 and DAP1 were distinguished to be greatly benefi-
cial to high-temperature fermentation in contrast to their

inferior parent alleles, due to their positive effects on
improving protective function of cell membrane against
thermal stress. This study improved our understanding
of genetic basis behind thermotolerance, and identified
more new causative genes linked to yeast thermotoler-
ance, thus providing more guidance to enhance thermo-
tolerance of industrial yeast strains.
Results
Selection of parent strains for genetic mapping
of thermotolerance
Total 124 natural, laboratory and industrial isolates of S.
cerevisiae collected in our lab (Additional file 1: Table S1)
were evaluated for their high-temperature fermentation
performances. The OD600 values representing cell growth
at 42 °C for 36 h ranged from 0.66 to 6.24 (Fig. 2a, Addi-
tional file 2: Table S2), showing that the strain ScY01
had the highest cell growth while W65 had the low-
est cell growth under thermal stress conditions. Mean-
while, ScY01 consumed the highest amount of glucose
(116.0 g/l) and produced the highest amount of ethanol
(57.3 g/l) at 42 °C (Fig. 2a, Additional file 2: Table S2). By
contrast, W65 almost had no glucose consumption and
ethanol production at 42 °C. ScY01 derived from the
industrial strain Ethanol Red through adaptive evolu-
tion at high temperature [11], whereas W65 is a natural
isolate. Cell growth profiles at 42 °C and 30 °C further
confirmed that ScY01 was significantly more thermotol-
erant than W65 at elevated temperature (Fig. 2b), while
both strains had no significant differences of cell growth
12:59

at normal temperature. Therefore, ScY01 and W65 were
chosen as the superior and inferior strains for genetic
mapping of thermotolerance, respectively.
Both ScY01 and W65 were separately sporu-
lated to generate the MATα and MATa haploid seg-
regants, named ScY01α and W65a (Additional file 1:
Table S1), respectively. To obtain stable haploids for
genetic mapping, the HO gene in ScY01α and W65a
were further knocked out by inserting zeocin- or
geneticin-resistance cassettes. The resulting haploid
parent strains were named ScY01α-tp and W65a-sp,
respectively.
Fig. 1 Scheme of identification of causative genes linked to the
thermotolerance phenotype. One superior thermotolerant strain
ScY01 and one
inferior thermosensitive strain W65 were selected from 124 S.
cerevisiae strains based on evaluation of thermotolerance. The
haploid segregants
of two parent strains with HO gene deletion were generated and
crossed to create the hybrid diploid strain tp/sp. Total 277
segregants were
sporulated from the hybrid strain and selected for the superior,
random and inferior pools based on evaluation of
thermotolerance. Genomic DNA
was extracted from these three pools as well as two parent
strains and the best and worst spores in the superior pool and
subjected to genome
resequencing. QTL mapping analyses were performed using the
EXPloRA and MULTIPOOL methods. To identify candidate
causative genes, the
SNPs in QTLs were annotated, and the nonsynonymous SNPs in
coding regions were sorted out according to their existences in
G28 and Z118 and

their manually checked frequencies using Integrative Genomics
Viewer (IGV ). Two major QTLs and two minor QTLs were
identified to originate from
the superior and inferior parent strain, in which five and three
genes contained nonsynonymous single nucleotide
polymorphism (SNP) variants,
respectively. Reciprocal hemizygosity analysis (RHA) and
allele replacement analysis further revealed two causative genes
in major QTLs and one
causative gene in minor QTLs
12:59
Screening of the superior, inferior and random pools
of segregants for genome sequencing
The parent haploid strains ScY01α-tp and W65a-sp
were crossed to obtain the hybrid diploid strain tp × sp
and then sporulated. Since ScY01α-tp and W65a-sp had
zeocin- or geneticin-resistance cassettes at HO locus,
successfully segregated haploid spores should only inherit
one drug resistance capacity of either zeocin or gene-
ticin. Combining with the subsequent diagnostic PCR for
the MAT locus, we isolated 107 haploid segregants on
geneticin selective plates and 170 haploid segregants on
zeocin selective plates. Total 277 haploid segregants were
isolated and tested for their thermotolerance capacities
to screen the ten most thermotolerant or thermosensitive
segregants for the superior pool and the inferior pool,
respectively, as well as ten random segregants for the ran-
dom pool for genome sequencing.
The distribution of the stress tolerance index (STI)
values (calculated as the ratio of the OD600 at 42 °C ver-

sus the OD600 at 30 °C measured at the 16-h time point)
in 277 haploid segregants is shown in Fig. 3a. Apparent
continuous variation as well as normal frequency distri-
bution of STI in the haploid segregants from the hybrid
tp × sp indicated yeast thermotolerance as a quantitative
trait. Among them, 49 segregants showed lower STI val-
ues (< 0.22) than the inferior parent W65a-sp, while 77
segregants showed higher STI values (> 0.38) than the
superior parent ScY01α-tp. Thus, ten segregants show-
ing the 10 lowest STI values (0.09 to 0.11) were selected
as the most thermosensitive segregants and assembled in
the inferior pool (Fig. 3a). To further narrow down supe-
rior segregants, cell growths of those 77 segregants at
42 °C were compared with ScY01α-tp (Fig. 3b). Among
them, 31 segregants showed higher cell growth than
ScY01α-tp at 42 °C. Thus, ten segregants showing the
ten highest OD600 ratios (1.37 to 2.17) than ScY01α-tp
were selected as the most thermosensitive segregants
and assembled in the superior pool. Finally, excluding the
segregants in superior and inferior pools, ten of the rest
segregants were randomly selected and assembled in the
random pool.
Additionally, fermentation capacities of the ten seg-
regants in the superior pool as well as parent strains
were evaluated (Fig. 3c). After 36 h incubation at 42 °C,
the thermotolerant parent strain ScY01α-tp consumed
68.6 ± 1.5 g/l glucose, produced 28.6 ± 1.1 g/l ethanol
and resulted in cell growth of 4.12 ± 0.04 OD600. By con-
trast, the thermosensitive parent strain W65a-sp, which
consumed 14.4 ± 0.3 g/l glucose, produced 6.0 ± 0.2 g/l
ethanol, and resulted in cell growth of 0.50 ± 0.01 OD600,
showing much lower fermentation capacity in contrast
to ScY01α-tp. The hybrid strain tp × sp exhibited higher
fermentation capacity than both the haploid parent

strains, which might be partially due to ploidy-driven
adaptation in cell physiology as previously reported [23].
Remarkably, two segregants G29 and G28 showed higher
capacities of glucose consumption and ethanol accu-
mulation than the hybrid strain tp × sp and the superior
parent ScY01α-tp, implicating unknown genetic factors
beyond the impacts of ploidy and the superior parent on
cell physiology. In addition, G28 showed slightly higher
ethanol accumulation than G29. On the other hand, the
segregant Z118 showed the worst fermentation capac-
ity. Thus, to facilitate QTL mapping based on pooled-
segregant whole-genome sequence analysis, the best and
the worst spores (G28 and Z118) from the superior pool
were also selected for genome sequencing.
Identification of QTLs and candidate causative genes
by pooled‑segregant whole‑genome sequence analysis
To identify the genetic basis underlying yeast thermo-
tolerance, seven samples, which were two parent strains
ScY01α-tp and W65a-sp showing distinct thermotol-
erance capacities, three segregant pools including the
superior, inferior and random pools derived from these
Fig. 2 Thermotolerance of 124 S. cerevisiae strains including
two
parent strains ScY01 and W65. a Cell growth (black bar),
consumed
glucose (red bar) and produced ethanol (blue bar) at 42 °C for
36 h.
Cells were grown in YP medium containing 200 g/l glucose. b
Cell
growth of two parent strains ScY01 and W65 at 42 °C and 30
°C. Cells
were grown in 100 ml Erlenmeyer flasks containing 50 ml YP
medium
with 200 g/l glucose. Data represent the mean and standard

error
of duplicate cultures at each condition (error bars are covered
by
symbols). Initial OD600 of 0.5 was used for all the
fermentations. In
panel b, data represent the mean and standard error of duplicate
cultures at each condition
12:59
two parents, and two individual segregants that were the
best and worst segregants G28 and Z118 in the supe-
rior pool, were subjected to whole-genome sequencing
for QTL mapping analysis. Single nucleotide polymor-
phisms (SNPs) in these seven samples were separately
extracted from their genomic alignments with the
sequence of the reference S288c genome. Total 35,459
quality-filtered and discordant SNPs from two parent
strains were used as genetic makers (Additional file 3:
Dataset S1). Usually, thermotolerance-related SNP vari-
ants in the superior pool are expected to dominantly
inherit from the superior parent. However, previous
studies have demonstrated the presence of recessive
mutations linked to yeast stress tolerance in the infe-
rior parent [5, 24], suggesting that the inferior parent
could also pass thermotolerance-related SNP variants
on to segregants in the superior pool. Therefore, we
detected the major QTLs originating from the superior
parent and the minor QTLs inherited from the inferior
parents, respectively. Correspondingly, the SNP variant
frequencies in the three segregant pools were calculated
as the percentages of the SNP nucleotides originating

Fig. 3 Selection of superior, inferior and random pools for
genome sequencing by evaluating thermotolerance capacities of
segregants. a The
distribution of the STI values in 277 haploid segregants from
the hybrid of the two parent haploid strains ScY01α-tp and
W65a-sp. Seventy-seven
segregants showing higher STI values (> 0.38) than the superior
parent ScY01α-tp were selected as superior segregants. Forty-
nine segregants
showing lower STI values (< 0.22) than the inferior parent
W65a-sp were selected as inferior segregants. Ten segregants
showing the ten lowest
STI values (0.09 to 0.11) were selected as the most
thermosensitive segregants and assembled in the inferior pool.
Cell growth experiments were
carried out in triplicates for each strain in 96-well plates with 1
ml YPD medium at 42 °C and 30 °C. b Cell growth comparison
of the 77 segregants
and W65a-sp with ScY01α-tp at 42 °C. Thirty-one segregants
showed higher cell growth than ScY01α-tp at 42 °C. Ten
segregants showing the ten
highest OD600 ratios (1.37 to 2.17) than ScY01α-tp were
selected as the most thermosensitive segregants and assembled
in the superior pool. c
Fermentation capacities of ten segregants in the superior pool at
42 °C. Fermentation experiments were conducted in 100 ml
Erlenmeyer flasks
containing 50 ml YP medium with 200 g/l glucose at 42 °C.
Consumed glucose, produced ethanol and cell growth were
measured after incubation
for 36 h. Data represent the mean and standard error of
duplicate cultures at each condition. Excluding the segregants in
superior and inferior
pools, ten of the rest segregants were selected and assembled in
the random pool

of 18Wang et al. Biotechnol Biofuels (2019)
12:59
from the superior or inferior parent for mapping major
or minor QTLs. The raw SNP frequencies were plotted
against the chromosomal position and smoothened by
using a Linear Mixed Model [25] (Fig. 4, upper panel).
Linkage analysis of QTLs was further performed using
the EXPLoRA and MULTIPOOL methods [26] (Fig. 4,
bottom panel). Overall, the numbers of QTLs identified
by these two methods were similar (Table 1; Additional
file 4: Dataset 2; Additional file 5: Dataset 3). However,
the average lengths of major and minor QTLs identified
by EXPLoRA were 47-kb or 69-kb, which were refined to
20-kb or 12-kb by MULTIPOOL (Table 1). Meanwhile,
the numbers of nonsynonymous variants and affected
genes were narrowed down.
To subtly identify candidate causative genes linked
to thermotolerance, all the SNP variants in the QTLs
identified by MULTIPOOL were localized to coding
and non-coding regions and annotated to be synony-
mous and nonsynonymous (Additional file 5: Dataset
S3). Furthermore, the nonsynonymous SNPs in cod-
ing regions, which in two sequenced individual spores
G28 and Z118 were similar to those in the parent
strains and also consisted with those in the superior
pool, were sorted out and manually checked for their
frequencies using Integrative Genomics Viewer (IGV)
[27–29]. We estimated that the SNP frequencies (count
of SNP-containing reads/total count of mapped reads)
in QTLs linked to thermotolerance could be high in the
superior pool, but low in the inferior pool, and simul-

taneously at the median value of around 0.5. Only the
variants in major QTLs meeting the criteria of allele
frequencies with ≤ 10% in the inferior pool, ≥ 75% in
the superior pool and around 50% in the random pool
as well as the variants in minor QTLs meeting the cri-
teria of allele frequencies with ≤ 25% in the inferior
pool, ≥ 75% in the superior pool and around 50% in
the random pool were considered to be causative vari-
ant candidates related to thermotolerance (Additional
file 5: Dataset S3). Therefore, the genes affected by
these causative variants were considered as candidate
causative genes, and the QTLs containing these can-
didate causative genes were fine-mapped (Table 2). In
total, two major QTLs, QTL1 and QTL2, were local-
ized on chromosome II and XII (Fig. 4a) and con-
tained two (RXT2 and VID24) and three affected genes
(ECM22, VPS34 and CSC1) by nonsynonymous causa-
tive variant candidates. Two minor QTLs, QTL3 and
QTL4, were localized on chromosome XV and XVI
(Fig. 4b) and contained two (IRA2 and AVO1) and
one (DAP1) affected genes by nonsynonymous causa-
tive variant candidates (Table 2). Total eight candidate
causative genes were identified by pooled-segregant
whole-genome sequence analysis.
Validation of causative genes in the QTLs
Reciprocal hemizygosity analysis (RHA) and allele
replacement analysis were, respectively, employed to
validate the eight candidate causative genes in the QTLs
(Table 2) based on the lethality and unavailability of their
gene deletions. RHA was used for five non-essential
genes including RXT2, VID24, ECM22, IRA2 and DAP1,
since their deletions were non-lethal. Allele replacement
was used for two essential genes including VPS34 and
AVO1, whose null alleles are inviable, as well as the CSC1
gene, whose deletion mutant was unavailable after sev-

eral rounds of attempts. For RHA, five pairs of hemizy-
gous diploid tp × sp hybrid strains were constructed
(Additional file 1: Table S1), in which each pair retained
a single copy of the superior (ScY01α-tp) or inferior
(W65a-sp) parent allele of RXT2, VID24, ECM22, IRA2
and DAP1, respectively, while the other copy of the gene
was deleted. For allele replacement analysis, three pairs
of allele homozygotes of diploid tp × sp hybrid strains
were constructed (Additional file 1: Table S1), in which
each pair contained two homogeneous gene allele from
the superior (ScY01α-tp) or inferior parent (W65a-sp)
allele of VPS34, AVO1 and CSC1, respectively. The fer-
mentation profiles of RHA and allele replacement strains
at high temperature were shown in Additional file 1:
Figure S1, and the diploid hybrid (tp × sp) of two parent
strains was used as a control. To have better quantitative
comparisons of fermentation capacities, the fermenta-
tion rates including maximum cell growth rate (μmax),
glucose-consumption rate (qsmax) and ethanol produc-
tivity (PEtOH) were calculated according to the fermen-
tation data in Additional file 1: Figure S1, and shown in
Fig. 5. From the results of RHA, compared with the con-
trol strain tp × sp, one of two hemizygotes for VID24 and
DAP1 showed significantly decreased cell growth or/
and fermentation capacities at high temperature (Fig. 5),
whereas both two hemizygotes for RXT2, ECM22 and
IRA2 showed increased thermotolerances to different
extent (Fig. 5). As for allele replacement analysis, com-
pared with the control strain tp × sp, one of two allele
homozygotes for VPS34 but both two allele homozygotes
for CSC1 and AVO1 showed significantly increased cell
growth or/and fermentation capacities at high tempera-
ture (Fig. 5). As for VPS34 and CSC1 in the major QTL2,
the allele homozygotes containing the variants from
the superior parent ScY01α-tp were expected to have
higher thermotolerance than the control stain or the

allele homozygotes containing the variants from the infe-
rior parent W65a-sp. As for AVO1 in the minor QTL3,
the homozygote containing the variant from W65a-sp
was expected to have higher thermotolerance than the
control stain or the allele homozygote containing the
variant from ScY01α-tp. Unexpectedly, the homozygote
12:59
Fig. 4 Mapping of major (a) and minor (b) thermotolerance-
related QTLs by pooled-segregant whole-genome sequence
analysis. a In major
QTL mapping, the SNP frequencies refer to the percentage of
the SNP nucleotide in the pools originating from the
thermotolerant parent
strain ScY01α-tp. b In minor QTL mapping, the SNP
frequencies refer to the percentage of the SNP nucleotide in the
pools originating from the
thermosensitive parent strain W65a-tp. In the upper panels of a
and b, scatter plots of SNP frequency versus chromosome are
shown. The raw data
of SNP frequencies are shown as dots, smoothened using a
Linear Mixed Model [30] and shown in bold lines. Green, red
and purple dots and lines
represent the raw data and smoothed data of SNP frequencies in
superior pool, inferior pool and random pool, respectively. In
the bottom panels
of a and b, QTL detections using the EXPLoRA and
MULTIPPOL methods are shown. The green line represents the
probability of linkage obtained
by EXPLoRA, where peak regions showed higher SNP
frequencies than 0.5 and were, therefore, detected as QTLs. The
red line represents LOD scores

in superior pool versus inferior pool calculated by
MULTIPOOL, whereas the purple line represents LOD scores in
superior pool versus random
pool. When both maximum LOD scores were higher than 5, this
locus was detected as a QTL by MULTIPOOL. QTLs were
further narrowed down
by analysing whether nonsynonymous amino acid changes were
present. Eventually, two major QTLs named QTL1 and QTL2 as
well as two minor
QTLs named QTL3 and QTL4 were identified
12:59
containing the CSC1 allele from W65a-sp showed oppo-
site results due to increased PEtOH (Fig. 5). Overall, all
these eight genes seemed to have impacts on high-tem-
perature fermentation performance.
The detailed results were as follows: VID24 was local-
ized in the major QTL of QTL1 (Table 2). Deletion of
the superior (ScY01α-tp) parent allele of VID24 in the
reciprocal hemizygote resulted in decreased cell growth
at high temperature but not significantly, and had sig-
nificant effects on qsmax and PEtOH at high-temperature
(Fig. 5). This result suggested the VID24 allele from the
superior strain might act as a causative and positive gene
in thermotolerance. VPS34 was in the major QTL of
QTL2 (Table 2). The allele homozygote containing two
copies of the VPS34D591E allele from the superior parent
showed significantly higher fermentation rates and capac-
ities at high temperature than the one containing two
copies of the inferior parent allele as well as the control
hybrid strain tp × sp (Fig. 5, Additional file 1: Figure S1).

Furthermore, our previous genome sequencing showed
that the diploid superior parent strain ScY01 has two
homogenous copies of the VPS34 D591E allele [30]. There-
fore, the VPS34D591E allele might be a causative gene in
thermotolerance. DAP1 was in the minor QTL of QTL4
(Table 2). The DAP1V39I mutant allele inheriting from
the inferior parent strain W65a-sp was found in the
superior thermotolerant pool (Table 2, Additional file 5:
Dataset S3). We estimated that the reciprocal hemizy-
gote containing the inferior parent allele of DAP1 might
have higher thermotolerance than the one containing the
superior parent allele of DAP1. Unexpectedly, the result
is quite the opposite. Compared with the control strain
tp × sp, the reciprocal hemizygote containing the inferior
parent allele of DAP1 showed significantly decreased fer-
mentation rates and capacities at high temperature, while
the one containing the superior parent allele of DAP1
exhibited significantly increased thermotolerance (Fig. 5,
Additional file 1: Figure S1). This result implicated that
the inferior parent allele of DAP1V39I might be a reces-
sive deleterious mutation in segregants of the superior
pool, while DAP1 might act as a recessive beneficial gene
in the superior thermotolerant parent. In terms of the
other five genes except for VID24, VPS34 and DAP1, the
hybrid control strain tp × sp containing their heterogene-
ous alleles showed lower high-temperature fermentation
performance than either the reciprocal hemizygotes only
retaining a single copy of allele or the allele homozygotes
containing two homogeneous copies of allele (Fig. 5,
Additional file 1: Figure S1). The extensive loss of hete-
rozygosity in S. cerevisiae genomes have been reported to
enable the expression of recessive alleles and generating
Table 1 QTL mapping by EXPLoRA and MULTIPOOL methods

Method Number of QTL Average length (kb) Number
of nonsynonymous
variants
Number
of affected
genes
Major QTL EXPLoRA 24 47 744 297
MULTIPOOL 22 20 292 119
Minor QTL EXPLoRA 13 69 521 233
MULTIPOOL 11 12 86 40
Table 2 Genes with nonsynonymous variants in two major
and two minor QTLs
QTLs Chr Start (bp) End (bp) Length (bp) LOD score Affected
gene Mutation (S288c genome as a reference)
ScY01α‑tp W65a‑sp
Major QTLs
QTL1 II 408,800 553,700 144,900 17 RXT2 332 G>C (R111G)
Wild type
VID24 154 C>T (P52S) Wild type
QTL2 XII 595,800 633,500 37,700 300 ECM22 1954 G>A
(G652S) Wild type
VPS34 1773 C>G (D591E) Wild type

CSC1 1126 C>A (Q376K) Wild type
Minor QTLs
QTL3 XV 174,500 184,900 10,400 272 IRA2 Wild type 7222
C>A (P2408T )
AVO1 Wild type 2558 T>C (V853A)
QTL4 XVI 228,200 238,100 9900 155 DAP1 Wild type 115
G>A (V39I)
12:59
novel allele combinations with potential effects on phe-
notypic diversity [31]. Thus, loss of heterozygosity in the
five gene alleles might play a similar function in contrib-
uting to high-temperature fermentation performance.
Overall, all the results suggested that these eight genes
were probably causative genes that linked to high-tem-
perature fermentation performance in S. cerevisiae,
although in different ways and to different extent.
Characterization of key causative gene alleles
for improving high‑temperature fermentation
of the industrial yeast
To further distinguish good targets from the eight
causative gene alleles for improving high-temperature
fermentation of the industrial yeast, we performed
principal component analysis (PCA) for high-temper-
ature fermentation data from all the RHA and allele
replacement strains at 42 °C in Additional file 1: Fig-

ure S1, including cell growth, glucose consumption
and ethanol production at all the time points during
fermentation. As shown in Fig. 6a, the first and second
accounted for 75.7% (PC1) and 10.5% (PC2) of the total
variation, respectively. The RHA and allele replace-
ment strains harbouring gene allele of VID24, VPS34
or DAP1 from the superior industrial parent ScY01α-tp
(red, blue and pink triangles in Fig. 6a), showing
enhanced high-temperature fermentation capacities in
contrast to the control stain tp × sp, were clearly sepa-
rated by the PCs from the RHA and allele replacement
strains containing those gene alleles from W65a-sp
(red, blue and pink circles in Fig. 6a). This result con-
firmed that the alleles of VID24, VPS34 and DAP1 in
the industrial yeast ScY01 could be greatly beneficial to
high-temperature fermentation. By contrast, as for the
rest five genes including RXT2, ECM24, CSC1, IRA2
and AVO1, the RHA and allele replacement strains har-
bouring their alleles from the parents were relatively
closely grouped by the PCs, although showing higher
high-temperature fermentation capacities than the con-
trol strain tp × sp. This result suggested that the alleles
of RXT2, ECM24, CSC1, IRA2 and AVO1 in the indus-
trial yeast ScY01 might play minor roles in supporting
Fig. 5 Identification of the causative genes using RHA and
allele replacement methods. a Maximum cell growth rate
(μmax). b
Glucose-consumption rate (qsmax). c Ethanol productivity
(PEtOH). The RHA and allele replacement strains are detailed
in Additional file 1: Table S1.
High-temperature fermentation capacities were evaluated at 42
°C in 100 ml Erlenmeyer flasks containing 50 ml YP medium
with 200 g/l glucose
at 220 rpm. Data represent the mean and standard error of

duplicate cultures at each condition. Statistical analysis for each
group of three strains
including the control strain tp × sp and two hemizygotes or
homozygotes of each gene was performed using one-way
ANOVA followed by Tukey’s
multiple-comparison posttest (***P < 0.001, **P < 0.01, *P <
0.05)
12:59
high-temperature fermentation. Most strikingly, the
PCs highly distinguished the RHA and allele replace-
ment strains harbouring the superior gene alleles of
VPS34 or DAP1 (Fig. 6a), suggesting that they would be
good targets for improving high-temperature fermenta-
tion of the industrial yeast.
VPS34 and VID24 have been reported to be involved in
the degradation of FBPase [32, 33], thus possibly affecting
trehalose accumulation. Furthermore, trehalose is
required on both sides of the lipid bilayer of membranes
for effective protection against thermal stress in S. cerevi-
siae [34]. Thus, we measured the trehalose levels in cells
of the RHA and allele replacement strains of VPS34 and
VID24 and the control strain tp × sp, which were grown
at thermal stress conditions (42 °C). Compared to the
control strain tp × sp, the allele homozygote containing
Fig. 6 Principal component analysis of high-temperature
fermentation data and physiological impacts of key causative
genes. a Principal
component analysis (PCA) of high-temperature fermentation

data from all the RHA and allele replacement strains, including
cell growth (orange
lines), glucose consumption (purple lines) and ethanol
production (turquoise lines) during fermentation (hours 0, 8, 12,
18, 24 30 36 42 and 48).
Means of biological repeats (in duplicates) are used. The gene
alleles originating from the superior (ScY01α-tp, triangle
symbols) and inferior
(W65a-sp, circle symbols) parents in the RHA and allele
replacement strains were colour-coded. b Trehalose
accumulation in the RHA and allele
replacement strains of VID24 and VPS34 at high temperature. c
Membrane fluidity of the RHA strains of DAP1 at high
temperature. The Membrane
fluidity is determined by the steady-state anisotropy of
fluorescent probe 1-[4-(trimethylamino)pheny]-6-phenyl-1,3,5-
hexatriene (TMA-DPH).
Yeast cells were grown at 42 °C in 100 ml Erlenmeyer flasks
containing 50 ml YP medium with 200 g/l glucose at 220 rpm.
For measuring trehalose
accumulation, cells were harvested after incubation for 36 h.
For determining membrane fluidity, cells were harvested after
incubation for 8 h, 16 h
and 36 h at the early-exponential, mid-exponential and
stationary phases, respectively. Data represent the mean and
standard error of duplicate
cultures at each condition. Statistical analysis in b and c was
performed using one-way ANOVA followed by Tukey’s
multiple-comparison posttest
(***P < 0.001, **P < 0.01, *P < 0.05)
12:59

two copies of the VPS34D591E allele from the superior
parent had significantly higher trehalose levels (Fig. 6b),
which was positively correlated with its enhanced high-
temperature fermentation capacities (Fig. 5, Additional
file 1: Figure S1). Similarly, the reciprocal hemizygote
containing the superior (ScY01α-tp) parent allele of
VID24 showed significantly higher trehalose levels than
the control strain, while the reciprocal hemizygote con-
taining the inferior (W65a-sp) parent allele of VID24
had significantly lower trehalose levels, positively corre-
lating with their enhanced high-temperature fermenta-
tion capacities (Fig. 5, Additional file 1: Figure S1). These
results indicated that the superior alleles of VPS34 and
VID24 might achieve beneficial effects on the high-tem-
perature fermentation capacities of the industrial yeast
by increasing trehalose levels.
DAP1 mutation leads to defects in sterol synthe-
sis, and thus influencing membrane fluidity [35, 36].
Cell wall and membrane are the first defence barrier
against environmental stresses. Negative correlation
between stress tolerance and membrane fluidity has
been observed for ethanol stress [37]. Therefore, we
determined the membrane fluidity of the reciprocal
hemizygotes of DAP1 and the control strain tp × sp by
measuring steady-state anisotropy of membrane-incor-
porated 1-[4-(trimethylamino)pheny]-6-phenyl-1,3,5-
hexatriene (TMA-DPH). High anisotropy values
indicate low membrane fluidity, allowing strong pro-
tection against environmental stresses, and vice versa.
The reciprocal hemizygote containing the superior
(ScY01α-tp) parent allele of DAP1 exhibited enhanced
high-temperature fermentation capacities (Fig. 5,
Additional file 1: Figure S1). Positively correlated, this
strain showed significantly higher anisotropy levels

at the early-exponential (8 h), mid-exponential (16 h)
phases than the control strain, indicating lower mem-
brane fluidity (Fig. 6c), thus providing effective pro-
tection against thermal stress to support active cell
metabolism, especially at the mid-log phase. By con-
trast, membrane fluidities of these cells at the station-
ary phase among the reciprocal hemizygotes of and the
control strains. These results suggested that the supe-
rior allele of DAP1 might achieve a beneficial effect on
the high-temperature fermentation capacities of the
industrial yeast by inhibiting membrane fluidity.
Based on characterization of key causative gene
alleles, we generated an overarching model integrat-
ing good targets for improving high-temperature fer-
mentation of the industrial yeast (Fig. 7). Remarkably,
we found that the physiological beneficial effects of the
superior (ScY01α-tp) parent alleles converged on cell
membrane. Vps34 and Vid24 from the superior par-
ent can contribute to trehalose accumulation at a high
level, thus providing more trehalose on both sides of
the lipid bilayer of membranes for effective protection
against thermal stress. On the other hand, Dap1 con-
taining Valine instead of Isoleucine at position 39 can
contribute to reduce membrane fluidity, thus providing
a strong defense barrier against thermal stress. Taken
together, our model supported the previous under-
standings that trehalose accumulation and reduced
membrane fluidity could promote high-temperature
fermentation in industrial yeast [34, 37], meanwhile
revealing VPS34 and DAP1 as good targets for further
Fig. 7 An overarching model integrating good targets for
improving high-temperature fermentation of the industrial yeast

of 18Wang et al. Biotechnol Biofuels (2019)
12:59
enhancing high-temperature fermentation of the indus-
trial yeast.
Discussion
Elevated thermotolerance is a highly valuable trait of
industrial yeasts that can substantially reduce the pro-
duction costs. Previous studies have identified several
causative genes and gained some insights into the under-
lying mechanism of this complex trait via various effi-
cient approaches, especially QTL methodology [5, 10,
12]. A major challenge of QTL analysis is to efficiently
identify minor QTLs linked to the inferior parent strain.
Since the phenotype is often masked by many subtle fac-
tors, for instance, epistasis [13], it is difficult to character-
ize the linkage between minor QTLs and the phenotype.
However, minor QTLs are unignorable, because they may
cause synergistic or additive effect, thus resulting in sig-
nificant effects on the related phenotype as major QTLs.
An efficient strategy has been used to reveal minor QTLs
by eliminating candidate QTLs in both superior and infe-
rior parent strains and repeatedly mapping the QTL with
pooled-segregant whole-genome sequence analysis [5].
This approach was further upgraded to be carried out
using relatively low numbers of segregants [20].
Based on the extensive pooled-segregant whole genome
sequence analysis, we successfully identified two major
QTLs (QTL1 and QTL2) and two minor QTLs (QTL3
and QTL4) localized on chromosome II, XII, XV, XVI,
respectively (Fig. 4, Table 2). Similar to previous study
[20], our work confirmed that relatively low numbers
of segregants can be used for successful QTL mapping

using pooled-segregant whole-genome sequence analy-
sis. Besides two methods of EXPLoRA and MUTIPOOL
used to detect QTLs, we also sequenced two individual
segregants from the superior pool and used IGV to man-
ually check SNP frequencies to facilitate more accurate
detection of QTLs closely associated with thermotoler-
ance. Four QTLs and eight nonsynonymous gene alleles
were narrowed down from dozens of QTLs and hun-
dreds of nonsynonymous SNP variants after QTL map-
ping, and finally validated to be causative factors related
to yeast thermotolerance (Additional file 4: Dataset 2,
Additional file 5: Dataset 3, Figs. 5, 6). Thus, the workflow
used in this study could be feasible and effective for QTL
mapping and identification of candidate causative genes.
Interestingly, among the eight validated causa-
tive genes, both VID24 and VPS34 were found to be
involved in translocation and degradation of fructose-
1,6-bisphosphatase (FBPase) in the vacuole. VID24
encodes a peripheral protein on vacuole import and
degradation (Vid) vesicles [38], which is required to
transfer FBPase from the Vid vesicles to the vacu-
ole for degradation [32]. VPS34 encodes the sole
phosphatidylinositol (Pl) 3-kinase in yeast, which is
essential for autophagy [39], which is also required for
the degradation of extracellular FBPase in the vacuole
import and degradation (VID) pathway [33]. When
yeast cells are out of glucose feeding for a long time,
Vps34 is induced and co-localized with actin patches
in starved cells. Once Vps34 is absent, FBPase and
the Vid24 associated with related actin patches before
and after re-feeding glucose. Strikingly, VID24 null
mutation leads to FBPase accumulation in the vesi-
cles, thus affecting trehalose synthesis [32, 40]. VPS34
null mutant also arrests FBPase with high levels in the

extracellular fraction. A previous study indicated treha-
lose is beneficial to protect cells from thermal stress in
S. cerevisiae [34]. Hence, we speculated that VID24 and
VPS34 might affect trehalose synthesis by controlling
the degradation of FBPase and thus be closely linked to
thermotolerance. As expected, we observed the posi-
tive correlation between the accumulation of trehalose
and the improvement of ethanol production due to the
existent of VID24 and VPS34D591E originating from the
thermotolerant parent strain ScY01α-tp (Fig. 6b). In
terms of testing the relationship between the degrada-
tion of FBPase and the improvement of ethanol produc-
tion, it would be worthwhile to be further investigated
in the future.
DAP1 was identified to be linked to thermotolerance
by minor QTL mapping (Fig. 4b). DAP1 encodes Heme-
binding protein and mutations lead to defects in mito-
chondria, telomeres, and sterol synthesis [35, 36], which
was closely associated with thermotolerance [10, 12]. The
abundance and composition of sterol plays a significant
modulatory role in yeast response to thermal stress by
affecting membrane fluidity [41]. Furthermore, the recip-
rocal hemizygote containing the superior allele of DAP1
showed increased high-temperature fermentation and
lower membrane fluidity in contrast to the control strain
(Fig. 6c). Thus, DAP1 might be involved in thermotoler-
ance by affecting sterol synthesis and membrane fluidity.
Furthermore, our results suggested DAP1 to be a reces-
sive causative gene linked to thermotolerance, which was
influenced by the genetic background. The mutant allele
of DAP1V39I from the inferior parent was validated to be a
recessive deleterious mutation for thermotolerance, since
the hemizygote containing the DAP1V39I allele showed
decreased high-temperature fermentation performance
compared to the hybrid control strain tp × sp (Fig. 5c).

Meanwhile, the wild-type DAP1 allele was validated to
be a recessive beneficial gene in the superior parent, since
the hemizygote containing the wild-type DAP1 allele
showed increased high-temperature fermentation per-
formance compared to the hybrid control strain tp × sp
(Fig. 5c). A previous study reported that mechanisms of
12:59
hydrolysate tolerance are very dependent on the genetic
background, and causal genes in different strains are dis-
tinct [24]. Our results confirmed that the effect of reces-
sive alleles or variants might be covered by different
genetic backgrounds and complementation of recessive
alleles could also contribute to the strain improvement.
Recent genome-wide association study revealed an
extensive loss of heterozygosity (LOH) associated with
phenotypic diversity across 1011 S. cerevisiae isolates
[31]. LOH could provide a driving force of evolution dur-
ing the adaptation of the hybrid strain to novel or stress-
ful environments by enabling the expression of recessive
alleles to potentially support the robustness of cells [42,
43]. In this study, based on RHA and allele replacement
analysis, positive effects of LOH on high-temperature
fermentation were observed for five causative genes
including RXT2, ECM24, CSC1, IRA2 and AVO1 identi-
fied by QTL mapping (Figs. 5, 6). Furthermore, we found
that the heterozygous forms of these five genes in the
control strain tp × sp seemed to have negative effects on
thermotolerance. This was different from the findings
that the beneficial mutations in heterozygous form seem-
ingly confer no benefit at the cellular level in nystatin

[44]. These results suggested that LOH would be an inter-
esting focus for QTL analysis studies.
Conclusions
We evaluated high-temperature fermentation perfor-
mances of 124 industrial, natural or laboratory S. cer-
evisiae strain and selected one superior thermotolerant
strain and one inferior thermosensitive strain as parent
strains. Pooled-segregant whole-genome sequence analy-
sis was performed for the selected three segregant pools
including the superior, inferior and random pools from
the hybrid of those two parent strains. Two individual
segregants in the superior pool were also sequenced to
facilitate the detection of nonsynonymous variants linked
to thermotolerance. Candidate causative genes were vali-
dated by RHA and allele replacement. Finally, two major
QTLs and two minor QTLs as well as eight causative
genes containing nonsynonymous SNP variants were
identified to be closely linked to yeast thermotolerance.
Strikingly, the superior parent alleles of VPS34, VID24
and DAP1 converged on cell membrane by increasing
trehalose accumulation or reducing membrane fluidity,
and thus beneficial to high-temperature fermentation of
the industrial yeast. Furthermore, LOH of five causative
genes including RXT2, ECM24, CSC1, IRA2 and AVO1
had positive effects on high-temperature fermentation,
suggesting that LOH would be an interesting focus for
QTL analysis studies. Overall, we identified novel caus-
ative genes linked to high-temperature fermentation
performance of yeast, providing guidelines to develop
more robust thermotolerant strain for the industrial pro-
duction of ethanol.
Methods
Strains, cultivation conditions and sporulation

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