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Industrial antifoam agents impair ethanol fermentation and induce stress responses in yeast cells

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Abstract and Figures

The Brazilian sugarcane industry constitutes one of the biggest and most efficient ethanol production processes in the world. Brazilian ethanol production utilizes a unique process, which includes cell recycling, acid wash, and non-aseptic conditions. Process characteristics, such as extensive CO2 generation, poor quality of raw materials, and frequent contaminations, all lead to excessive foam formation during fermentations, which is treated with antifoam agents (AFA). In this study, we have investigated the impact of industrial AFA treatments on the physiology and transcriptome of the industrial ethanol strain Saccharomyces cerevisiae CAT-1. The investigated AFA included industrially used AFA acquired from Brazilian ethanol plants and commercially available AFA commonly used in the fermentation literature. In batch fermentations, it was shown that industrial AFA compromised growth rates and glucose uptake rates, while commercial AFA had no effect in concentrations relevant for defoaming purposes. Industrial AFA were further tested in laboratory scale simulations of the Brazilian ethanol production process and proved to decrease cell viability compared to the control, and the effects were intensified with increasing AFA concentrations and exposure time. Transcriptome analysis showed that AFA treatments induced additional stress responses in yeast cells compared to the control, shown by an up-regulation of stress-specific genes and a down-regulation of lipid biosynthesis, especially ergosterol. By documenting the detrimental effects associated with chemical AFA, we highlight the importance of developing innocuous systems for foam control in industrial fermentation processes. Electronic supplementary material The online version of this article (10.1007/s00253-017-8548-2) contains supplementary material, which is available to authorized users.
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APPLIED MICROBIAL AND CELL PHYSIOLOGY
Industrial antifoam agents impair ethanol fermentation
and induce stress responses in yeast cells
Jens Christian Nielsen
1,2,3
&FelipeSennedeOliveiraLino
1
&
Thomas Gundelund Rasmussen
1
&Jette Thykær
2
&Christopher T. Workman
2
&
Thiago Olitta Basso
1,4
Received: 17 May 2017 /Revised: 6 September 2017 /Accepted: 17 September 2017 /Published online: 10 October 2017
#Springer-Verlag GmbH Germany 2017
Abstract The Brazilian sugarcane industry constitutes one of
the biggest and most efficient ethanol production processes in
the world. Brazilian ethanol production utilizes a unique pro-
cess, which includes cell recycling, acid wash, and non-
aseptic conditions. Process characteristics, such as extensive
CO
2
generation, poor quality of raw materials, and frequent
contaminations, all lead to excessive foam formation during
fermentations, which is treated with antifoam agents (AFA).
In this study, we have investigated the impact of industrial
AFA treatments on the physiology and transcriptome of the
industrial ethanol strain Saccharomyces cerevisiae CAT-1.
The investigated AFA included industrially used AFA ac-
quired from Brazilian ethanol plants and commercially avail-
able AFA commonly used in the fermentation literature. In
batch fermentations, it was shown that industrial AFA com-
promised growth rates and glucose uptake rates, while com-
mercialAFAhadnoeffectinconcentrations relevant for
defoaming purposes. Industrial AFA were further tested in
laboratory scale simulations of the Brazilian ethanol produc-
tion process and proved to decrease cell viability compared to
the control, and the effects were intensified with increasing
AFA concentrations and exposure time. Transcriptome analy-
sis showed that AFA treatments induced additional stress re-
sponses in yeast cells compared to the control, shown by an
up-regulation of stress-specific genes and a down-regulation
of lipid biosynthesis, especially ergosterol. By documenting
the detrimental effects associated with chemical AFA, we
highlight the importance of developing innocuous systems
for foam control in industrial fermentation processes.
Keywords Bioethanol .Antifoam .Fermentation .
Sugarcane .Saccharomyces cerevisiae .Brazilian
ethanol process
Introduction
Brazil has been at the frontline of the bioethanol industry since
the 1970s and the sugarcane-based production process
employed in Brazil constitute one of the most cost-efficient
ethanologenic processes (Walker 2010). Having produced
about 27 billion liters of ethanol in 2015, Brazil is the second
largest ethanol producer in the world, only exceeded by the
USA (Renewable Fuels Association 2016). The production
process employed in Brazil is unlike other ethanol processes
and includes yeast recycling, acid wash of the yeast biomass,
and non-aseptic conditions, which altogether result in a con-
tinuously changing and highly stressful environment (Della-
Bianca et al. 2013). Despite this, the Brazilian bioethanol pro-
cess shows a high efficiency with yields up to 93% of theoretic
stoichiometric conversion (Amorim et al. 2011).
Foam formation is considered one of the main draw-
backs of industrial fermentation processes, especially on
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s00253-017-8548-2) contains supplementary
material, which is available to authorized users.
*Christopher T. Workman
cwor@dtu.dk
*Thiago Olitta Basso
thiagobasso@usp.br
1
Novozymes Latin America Ltda, 83707-660 Araucária, Brazil
2
Department of Biotechnology and Biomedicine, Technical
University of Denmark, DK2800 Kgs. Lyngby, Denmark
3
Present address: Department of Biology and Biological Engineering,
Chalmers University of Technology, SE412 96 Gothenburg, Sweden
4
Present address: Department of Chemical Engineering, Polytechnic
School, University of São Paulo, 05508-010 São Paulo, Brazil
Appl Microbiol Biotechnol (2017) 101:82378248
https://doi.org/10.1007/s00253-017-8548-2
large-scale processes, and Brazilian ethanol fermentations
are no exception. Foam consists of gas bubble dispersions
on liquid, solid/liquid, and solid systems (Eisner et al.
2007). A number of features greatly influence its occurrence
in bioreactors, including medium composition, gas introduc-
tion/formation, and strain-specific characteristics of the pro-
duction organism (Junker 2007). During ethanol fermenta-
tion by Saccharomyces cerevisiae strains, one of the main
factors affecting foaming is the yeast itself. Foam stability
is strongly influenced by the proteins adsorbing the gas/
liquid interface (Prins and Van Riet 1987), where
mannoproteins from the yeast cell wall have shown to in-
crease the foam stability to a great extent (Blasco et al.
2011). In the Brazilian ethanol setup, large volumes of bio-
mass are used (about 10% w v
1
), in a cell recycling system,
and screening strategies have focused in selecting yeast with
non-foaming phenotypes (Basso et al. 2008). However, since
the process is non-sterile, the appearance of wild yeast strains
with foaming phenotypes occurs regularly as contaminants
resulting in increased foam formation. Having a yeast strain
with a foaming phenotype is highly deleterious for the pro-
cess economics, since a larger volume of the vessel is taken
up by the foam, and it increases overall production costs, due
to consumption of antifoam agents (AFA). This is especially
important for production of low-value products such as eth-
anol (Basso et al. 2008).
Different chemical antifoams have different impacts on
microbial physiology and cell growth (Routledge and Bill
2012), and the choice of the chemical used should take
this into consideration. Several studies have considered
the impact of AFA on cell physiology mainly as a result
of the physicochemical effects on medium, such as reduc-
tion of the liquid surface tension, and decreased oxygen
transfer rate (Morao et al. 1999; Garcia-Ochoa and Gomez
2009; Routledge et al. 2011). Still, some studies demon-
strate a more direct impact on microbial physiology by the
addition of AFA. One widely used AFA based on poly-
propylene glycol was shown to have toxic effects on
Bacillus thuringiensis during sewage sludge fermentation
for biopesticide production, by affecting the transport of
nutrients and oxygen through the cell walls (Vidyarthi
et al. 2000). Another AFA based on silicone polymers
was shown to stimulate glycerol production, at the ex-
pense of ethanol, in S. cerevisiae steady-state chemostat
cultures, under conditions of low oxygen and excess glu-
cose (Grosz and Stephanopoulos 1990). These studies
demonstrate that selection of AFA for a particular process,
should take into consideration the physiological impact it
might have on the microbial cell culture in a context de-
pendent manner, rather than only the cost of the chemical.
This study has investigated the impact of AFA on the phys-
iology and transcriptome of the industrial ethanol S. cerevisiae
strain CAT-1. We show that while commercially available
AFA used in the yeast fermentation literature are innocuous
at concentrations relevant for defoaming, industrial AFA used
at Brazilian ethanol plants have severe negative impacts on the
production performance of S. cerevisiae. Effects of AFA treat-
ments were investigated in both defined and in molasses-
based media, and transcriptome analysis suggested that alter-
ations in lipid metabolism, as a consequence of AFA expo-
sure, might account for a decreased stress tolerance in
Brazilian ethanol fermentations.
Materials and methods
Strain and media
The industrial S. cerevisiae strain CAT-1 (CBMAI 0957,
Coleção Brasileira de Microrganismos de Ambiente e
Indústria) was used for all fermentation assays in this study.
Single colonies were picked from plates and used for inocu-
lation of precultures in yeast extract peptone dextrose (YPD)
(containing 10 g l
1
yeast extract, 20 g l
1
peptone, and
20 g l
1
glucose) or yeast nitrogen base (YNB) (BD Difco
cat. no. 291940) media and incubated at 32 °C and 200 rpm
overnight. Sugarcane must was prepared by diluting sugar-
cane molasses in tap water to 20 °Brix, followed by centrifu-
gation to remove solids. All media were autoclaved for steril-
ization. Water-soluble AFA were diluted in sterile
demineralized water while water-insoluble AFA were diluted
in 99% ethanol and added to the fermentation media. Ethanol
was added to appropriate media to compensate for the ethanol
contribution from ethanol-dissolved AFA. Industrial AFA
samples (denoted as Ind_B and Ind_Z) are composed of two
products, and they were used in all cultivations as a mixture of
an antifoam product (AF_B and AF_Z, respectively) and a
dispersantproduct (D_B and D_Z, respectively) at 1:1 propor-
tion (Table 1).
Batch fermentation
All fermentations were inoculated from a preculture to an
initial OD
600
of 0.1 and incubated at 32 °C. Small-scale
fermentations were conducted in 96-well Costar flat-
bottom microtiter plates with a working volume of
100 μl YPD medium and linear shaking on Bfast^using
a Biotek Synergy Mx plate reader (Winooski, VT, USA).
Growth was monitored by measuring OD
600
every
15 min. Shake flask fermentations were conducted in
500-ml Erlenmeyer flasks in YNB medium, with
200 rpm orbital shaking and samples for biomass and
metabolite quantification were collected every 1.5 h. All
fermentations were conducted in biological triplicates.
8238 Appl Microbiol Biotechnol (2017) 101:82378248
Yeast propagation and fed-batch fermentation with cell
recycling
To propagate sufficient yeast biomass for inoculation of mo-
lasses fermentations, a YPD preculture was incubated over-
night at 32 °C without shaking. YPD cultures were subse-
quently fed in discrete portions with propagation medium
(sugarcane must diluted to 10 °Brix and enriched with yeast
extract (5 g l
1
) and peptone (10 g l
1
)) to a final volume of 2 l
during the course of 3 days.
Fed-batch fermentations representing the typical industrial
process were carried out in 50-ml falcon tubes. In the first
cycle, cells from the propagation culture were added to each
tube in an amount corresponding to 8% (w v
1
) of the final
volume. Cells were fed with 25 ml sugarcane must in three
equal-sized portions with 1.5 h intervals. Cultures were incu-
bated for 7 h at 32 °C without agitation and left at room
temperature overnight. In the following day, cells were sepa-
rated from the fermentation wine by centrifugation (3220 rcf,
5 min) and resuspended in wine (30% wet weight w w
1
)to
simulate the industrial centrifuge efficiency. Cells were further
diluted in demineralized water (1:1) before addition of 1 M
sulphuric acid to a final pH of 2.5. After incubation in acid at
room temperature for 1 h, feeding of sugarcane must was
initiated, restarting the process. Industrial AFA were adminis-
tered as in the industry, with the dispersant added during the
acid wash and antifoam after 1 h of fermentation.
Metabolite concentrations in the fermentation wine were
determined with high-performance liquid chromatography
(HPLC) and biomass as wet weight. Moreover, CO
2
for-
mation was followed during the fermentation by measuring
the weight loss of the fermentation tubes and assuming all
loss of weight could be attributed to CO
2
production.
Yields were calculated on a C-mole basis relative to total
reducing sugars, i.e., sucrose, glucose, and fructose. The
wine returned, and hence the addition of extracellular
metabolites from the previous fermentation, was taken into
account and corrected for in the yield calculations.
Metabolite quantification
Metabolite concentrations were determined by high-
performance liquid chromatography (HLPC). Sugars (sucrose,
glucose, and fructose) were separated on an Aminex HPX-87H
ion-exclusion column (Hercules, CA, USA), isocratically eluted
at 25 °C with 5 mM H
2
SO
4
as the mobile phase at a flow rate of
0.6 ml min
1
. Glycerol, ethanol, and organic acids were deter-
mined similarly, but with a higher flow rate (0.7 ml min
1
)and
temperature (65 °C). Detection was performed
refractrometrically. For the sugarcane fermentations, ethanol
was measured by distillation of 10 ml fermentation wine and
subsequent density determination using an Anton Paar electron-
ic density meter (Graz, Austria) (Basso et al. 2008).
Determination of cell viability
Samples from sugarcane fermentations were diluted 1000
times in demineralized water to a final concentration of ap-
proximately 500 cells ml
1
. Cells were dyed with propidium
iodide (PI) (215 nmol ml
1
) and incubated for 510 min in the
dark before applying the samlpes on a flow cytometer Accuri
C6 (BD, Franklin Lakes, NJ, USA) according to the manufac-
turers recommendations. Threshold of FSC-H gate was set to
200,000 to avoid large particles. Quantification of viability
was done by separating two populations generated from the
histogram of the PI fluorophore detector FL3-H.
RNA extraction, sequencing, and quantification
Samples for transcriptome analysis were collected from cycle
5 of the sugarcane fermentations, after 4 h of incubation, spun
down, resuspended in RNAlater (Qiagen, Hilden, Germany,
Tabl e 1 Commercial and industrial antifoam agents (AFA) used in this study
Product Fraction Active ingredient(s) Source
Antifoam 204 Single Polypropylene-based polyether dispersion Commercial sample
(Sigma-Aldrich, St. Louis, MO, USA)
Antifoam C Single Aqueous emulsion with 30% silicone Commercial sample
(Sigma-Aldrich, St. Louis, MO, USA)
P2000 Single Polypropylene glycols Commercial sample
(Sigma-Aldrich, St. Louis, MO, USA)
Ind_B Antifoam AF_B Emulsion of mineral oil and glycols Industrial sample
(Aratrop Industrial, São Paulo, Brazil)Dispersant D_B Polyether glycol
Ind_Z Antifoam AF_Z Vegetable oil, polyglycols Industrial sample
(Alcolina, São Paulo, Brazil)Dispersant D_Z Polypropylene and polyethylene glycols
Single: product is composed of one single fraction
AF_B antifoam fraction of product Ind_B, D_B dispersant fraction of product Ind_B, AF_Z antifoam fraction of product Ind_Z, D_Z dispersant fraction
of product Ind_Z
Appl Microbiol Biotechnol (2017) 101:82378248 8239
cat. no. 76106) and incubated for 1 h, at 4 °C. Subsequently,
cells were pelleted and stored at 80 °C until further analysis.
Biomass samples were lysed on a FastPrep instrument (Lysing
Matrix Y from MP Biomedicals, Hampton, NH, USA, cat. no.
116960050) and total RNA extracted according to the manu-
facturers recommendations (TRIzol Plus RNA purification
kit, Invitrogen, Carlsbad, CA, USA, cat. no. 12183-555).
cDNA libraries were prepared using Illumina TruSeq kit
(Illumina, San Diego, CA, USA, cat. no. RS-122-2001) with
enzymatic rRNA depletion. Sequencing was done using
Illumina HiSeq 2000 technology yielding an average of
26 M, 99-nt long paired-ends reads per sample, with an aver-
age insert distance of 300 nts. Reads in fastq format were
quality assessed using FastQC (v0.10.1) (Andrews 2012)
and trimmed according to varying GC-content at the 5and
3ends, and a quality cut-off (Q< 20). Trimmed sequence
reads were mapped to the S. cerevisiae s288C reference ge-
nome (version EF4, Ensembl) (Engel et al. 2014)using
Tophat (v2.0.9) (Kim et al. 2013), and transcripts were quan-
tified on a count basis using HTSeq (v0.6.0) (Anders et al.
2015)withBintersection-strict^mode for overlapping reads.
Data normalization and calculation of gene level statistics
were conducted using the R package DESeq2 (Love et al.
2014), and significantly differentially expressed genes were
identified as having Benjamini-Hochberg-corrected pvalues
less than 0.01. Transcripts with overall counts less than 15
among all samples were removed for the further analysis.
The transcriptome data discussed in this publication have been
deposited in NCBIs Gene Expression Omnibus and are ac-
cessible through the GEO Series accession number
GSE103004.
Gene-set analysis
Gene sets with amiGO gene ontology (GO) terms were
downloaded from Ensembl (Yates et al. 2016), and gene-
metabolite associations for reporter metabolite analysis were
extracted from the yeast 7 genome-scale metabolic model
(Aung et al. 2013). Gene set analysis was performed with
the normalized transcript counts using the R package Piano
(Varemo et al. 2013), and gene sets with Benjamini-Hochberg
corrected p-values less than 0.01 were considered significant.
Linear models
Linear models were applied to test whether mRNA levels
were a linear function of cell viability. Modeling was per-
formed using the R implementation of an ANOVA Ftest with
p< 0.01 as criterium to reject the null-hypothesis, i.e., that
gene expression and viability were uncorrelated. Enrichment
of GO terms among genes with significant positive or negative
correlations was tested with all expressed transcripts as
background, using the hypergeometric test implemented in
amiGO (Carbon et al. 2009).
Results
The differential effects of commercial and industrial
antifoam agents on yeast physiology under defined
laboratory conditions
In this study, a total of seven AFA were utilized and a
description of each is provided in Table 1. Three commer-
cial AFA were selected as commonly used in the fermen-
tation literature for yeast physiological studies (Brochado
et al. 2010; Routledge et al. 2011; Basso et al. 2011),
while four industrial AFA were acquired as samples from
two Brazilian ethanol plants and are currently being used
in the Brazilian ethanol industry. As two AFA are used in
conjunction to control foam in the industrial process
(Basso et al. 2008; Della-Bianca et al. 2013), the four
industrial AFA represent two combination treatments for
defoaming (Ind_B and Ind_Z). These combination treat-
ments consist of one dispersant (D_B or D_Z) which is
added into the yeast slurry prior feeding, to prevent foam
formation, and an antifoam (AF_B or AF_Z) which is
added when foam formation occurs during fermentation
(Supplementary Fig. S1).
A physiological characterization of the industrial yeast
S. cerevisiae strain CAT-1 was performed initially in a micro-
plate reader setup, in order to assess the effect of the AFA on
growth rate at different concentrations. Secondly, a more de-
tailed investigation was done using shake-flask batch cultiva-
tions in defined medium (YNB) to study the impact of indus-
trial AFA on major physiological parameters, such as product
yields.
Microplate cultivations in YPD medium with 10 g l
1
glucose were used to estimate the effect of different con-
centrations of AFA on the maximum specific growth rate
(μ
max
). In the case of commercial AFA, concentrations up
to 100 times the manufacturers recommendation were
tested. The presence of Antifoam C and P2000 decreased
growth rates as compared to the control (no AFA added)
in concentrations of 750 and 7500 mg l
1
, respectively,
while Antifoam 204 displayed no effect on growth rate up
to 375 mg l
1
(Fig. 1a) (higher concentrations of
Antifoam 204 did not emulsify and hence were not test-
ed). Assessing the industrial AFA revealed that both treat-
ments start to reduce growth rate at 60 mg l
1
(Fig. 1b),
which corresponds to the average concentration used in
the industrial setting (Pecege-Esalq 2012). To further in-
vestigate how metabolite production and growth charac-
teristics were influenced by the two industrial AFA,
shake-flask cultivations were carried out in YNB
8240 Appl Microbiol Biotechnol (2017) 101:82378248
containing 10 g l
1
glucose. Under these conditions, while
yield coefficients virtually remained unchanged by the
presence of industrial AFA, specific metabolite production
and consumption rates were negatively affected in view of
the reduced growth rates (Fig. 1c).
Industrial AFA impair cell viability and fermentation
kinetics at molasses-based fermentation with cell recycle
After this initial evaluation, the influence of industrial AFA
on the fermentation performance of S. cerevisiae was inves-
tigated at the physiological and at the transcriptional levels,
in a setup that mimicked as far as possible the industrial
ethanol process employed in Brazil. The two industrial
defoaming treatments currently used in the Brazilian
bioethanol industry (Ind_B and Ind_Z) were assessed and
benchmarked against a control experiment without the ad-
dition of AFA (Table 1). The fermentations were conducted
as laboratory-scale replications of the industrial production
process, as described elsewhere (Basso et al. 2014;
Raghavendran et al. 2017) (Supplementary Fig. S1). In
brief, this included yeast cell recycle, acid wash, and fed-
batch cultivations in sugarcane must for five consecutive
fermentation cycles. For all fermentations, the loss of CO
2
was monitored continuously and fermentation yields were
determined. The two components of the industrial
defoaming treatments were added separately as done in in-
dustry, with dispersants (D_B or D_Z) added during the acid
wash, while antifoams (AF_B or AF_Z) were added after
1 h of fermentation, where foam issues are more prominent
in industrial fermentations (H.V. Amorim, pers. comm.).
When simulating the industrial fermentation conditions,
the overall fermentation yields on ethanol, CO
2
, glycerol,
and acetate showed no significant differences among the treat-
ments. Likewise, residual sugar levels were quite similar
among conditions (Table 2). The biomass gain along the cul-
tivation cycles was virtually absent (data not shown), which is
in agreement with observations under industrial conditions,
especially when a molasses-based must is used (Amorim
et al. 2011). The carbon balance for all fermentations reached
8790% of the total carbon input; however, the missing car-
bon can be attributed to ethanol evaporation and an underes-
timation of the CO
2
formation, since CO
2
loss was estimated
by weighing the flasks, and could not be properly monitored
during feeding and antifoam addition periods.
In the first cycles, both industrial AFA treatments (Ind_B
and Ind_Z) showed cumulative CO
2
production levels com-
parable to the control, although a reduction was observed in
the later cycles (cycle 4 and cycle 5). Treatment Ind_Z im-
posed the greatest CO
2
reduction, showing cumulative pro-
duction levels corresponding to 52% of the control, while
Ind_B treated cells were producing 77% of the CO
2
in the
control (Fig. 2a). The CO
2
evolution over time showed that
the CO
2
reduction was mainly apparent in the beginning of the
fermentations where an increased lag phase was observed for
the AFA treatments (Fig. 2b). Ethanol was not quantified dur-
ing fermentations (only at the end of each cycle), but it can be
assumed that the CO
2
formation has a 1:1 stoichiometry to
ethanol production. Based on this assumption, ethanol
b
c
a
Fig. 1 The effect of commercial and industrial AFA on growth rate and
product yields of S. cerevisiae CAT-1 cultures. aMaximum specific
growth rate (h
1
) in YPD medium in microplate reader cultures in the
presence of different concentrations of three commercial AFA samples. b
Maximum specific growth rate (h
1
) in YPD medium in microplate reader
cultures in the presence of different concentrations of two industrial AFA
samples (products Ind_B and Ind_Z are composed of an antifoam and a
dispersant, at equal amounts). cProduct yields during growth of
S. cerevisiae CAT-1 in shake-flask cultures on YNB with 10 g l
1
glucose
as sole carbon source, in the presence of AFA Ind_B (30 mg l
1
AF_B
and30mgl
1
D_B) and Ind_Z (30 mg l
1
AF_Z and 30 mg l
1
D_Z).
Yield o n bi omas s (Y
sx
), on ethanol (Y
se
), on glycerol (Y
sg
) and on acetate
(Y
sa
). Values represent the average of triplicate experiments ± standard
deviations
Appl Microbiol Biotechnol (2017) 101:82378248 8241
productivity was reduced in the same proportion as the CO
2
levels, and hence resulted in a reduced ethanol yield after 7 h
of fermentation (in the industrial process, the fermentations
are typically terminated after 79h).ThefinalCO
2
emission
(after ~ 24 h) reached the same level for all treatments empha-
sizing that the total yield is not affected.
For all treatments, the viability declined for each fermenta-
tion cycle as expected (Basso et al. 2008), highlighting the
stressful nature of the process (Fig. 2c). The viability profile
showed a similar pattern as the CO
2
emission, with the indus-
trial AFA treatments reducing viability compared to the con-
trol. Viability results explain the underlying reason for the
observed reduction in CO
2
levels, as fewer living cells were
present to produce CO
2
. This trend of reduced viability was
consistent for all industrial AFA treatment experiments per-
formed and moreover, proved to be concentration dependent
(data not shown).
Transcriptome analysis reveals that AFA induce
additional stress responses in yeast cells during industrial
conditions
In order to better understand the physiological perturbations
caused by the AFA treatments, samples were collected for
transcriptome analysis 4 h after initiation of the last fermenta-
tion cycle (cycle 5), which was 1 h after the feeding had
ended. RNA sequencing was performed using an Illumina
HiSeq instrument with the paired-end method. Considering
that the quality of the published genome assembly of the strain
CAT-1 was not suitable as a reference for read mapping
(Babrzadeh et al. 2012), reads were aligned to the genome
of the laboratory strain S. cerevisiae S288C. For each sample,
an average of 78.3% ± 2.1 of the sequencing reads were suc-
cessfully mapped to the reference genome. The relatively low
mapping frequency was anticipated due to the distant relation
between CAT-1 and the reference strain S288C (Babrzadeh
et al. 2012). Reads mapping to open reading frames in
S288C were quantified, and a total of 6357 transcripts were
identified as being expressed in at least one of the samples.
Quality and reproducibility of the RNA-seq samples were
assessed by a pairwise comparison of transcript levels for each
replicate and showed a Pearson correlation coefficient of at
least 0.94 between all samples (Supplementary Fig. S2).
To assess the global effects of the treatments on gene ex-
pression, a principal component analysis (PCA) was conduct-
ed based on all measured transcript levels (Fig. 3aand
Supplementary Fig. S3). The PCA showed that the replicates
of each treatment clustered together as expected, except for
one of the control treatments which was deviating on the first
PC, although was comparable on the second PC. All samples
were included in the analysis since the results proved to be
similar to excluding the deviating control sample. The relative
effects of the treatments were in agreement with the observed
physiological changes with the Ind_B treatment resulting in
only minor changes, while the Ind_Z treated cells showed
more drastic changes in expression levels, as seen by its rela-
tive distance to the control in the first two PC dimensions. A
differential expression analysis further confirmed the relative
pertubations caused by the AFA treatments as a total of 297
genes in Ind_B and 439 genes in Ind_Z were significantly
differentially expressed relative to the control (adjusted pval-
ue < 0.01). Among these genes, a total of 69 were up-
regulated and 42 were down-regulated in both species (Fig. 3).
To identify the most apparent transcriptional responses to
the AFA treatments and to group the affected genes based on
function, gene set analysis was conducted with the normalized
transcript counts for each treatment in comparison to the un-
treated cells (Supplementary Data 1). Gene sets included gene
ontology (GO) terms and metabolites connected to genes
through the corresponding enzymatic reaction and identified
using the reporter feature algorithm (Patil and Nielsen 2005).
In addition to these analyses, linear models were applied be-
tween the gene expression of each gene in a sample relative to
the cell viability observed in the sample, in order to identify
gene expression patterns that could be explained by the phys-
iological response. The main transcriptional responses identi-
fied are summarized in Fig. 4.
The most significantly enriched GO term among the up-
regulated genes in Ind_B was transmembrane transport
(GO:0055085), and some of these transporters were related
Tabl e 2 Physiological parameters during yeast fed-batch fermentation of sugarcane must. Yield coefficients and residual sugars in the 5
th
fermentation
cycle of a fed-batch culture of strain S. cerevisiae CAT-1 with no AFA added (control) and with the addition of two industrial AFA (Ind_B and Ind_Z).
All yields were calculated based on total consumed sugars (sucrose, glucose, and fructose)
Condition Ethanol yield
(C-mol C-mol
1
)
CO
2
yield
(C-mol C-mol
1
)
Glycerol yield
(C-mol C-mol
1
)
Acetate yield
(C-mol C-mol
1
)
Residual sugars (g l
1
) C-balance*
Control 0.577 ± 0.005 0.289 ± 0.003 0.003 ± 0.000 0.001 ± 0.000 5.48 ± 0.05 0.870 ± 0.007
Ind_B 0.593 ± 0.006 0.297 ± 0.003 0.002 ± 0.000 0.001 ± 0.000 6.16 ± 0.72 0.894 ± 0.009
Ind_Z 0.584 ± 0.002 0.292 ± 0.001 0.003 ± 0.000 0.001 ± 0.000 5.84 ± 0.26 0.881 ± 0.003
Values represent the average ± standard deviation from triplicate experiments
*Carbon balance was calculated as the sum of all yield coefficients
8242 Appl Microbiol Biotechnol (2017) 101:82378248
to sulfur metabolism, i.e., S-methylmethionine transport
(GO:0015806) and cysteine transport (GO:0042883). In addi-
tion, other sulfur assimilation processes like sulfonate
dioxygenase activity(GO:0000907) and sulfur compound cat-
abolic process (GO:0044273) were enriched. For Ind_Z other
GO terms related to sulfur metabolism was enriched among
up-regulated genes, including cysteine-and methionine
biosynthetic process (GO:0019344/0009086) and sulfate as-
similation (GO:0000103). The main up-regulated genes with-
in the mentioned GO terms were JLP1 in the Ind_B treatment
and MET3 in Ind_Z (Fig. 4). JLP1 encodes an
Fe(II)dependent sulfonate/alpha-ketoglutarate dioxygenase
which has been shown to be induced under sulfur starvation
(Zhang et al. 2001) and is believed to make use of aliphatic
sulfonates such as taurine, cysteate, and isethionate as alterna-
tive sulfur sources (Hogan et al. 1999). MET3 catalyzes the
primary step in the intracellular sulfate activation, where sul-
fate is reduced to sulfide which is involved in cysteine and
methionine metabolism (Gierest et al. 1985). Interestingly,
both up-regulated genes propose that sulfur starvation is tak-
ing place in both treatments, but also that the cells attempt to
cope with the stress using different strategies: Ind_B-treated
cells, by recycling intracellular sulfur and Ind_Z-treated cells
by increasing the assimilation of extracellular sulfate.
Investigating the significantly overrepresented reporter metab-
olites within these treatments revealed sulfur-containing com-
pounds as well. For Ind_B, sulfate, S-methylmethionine, tau-
rine, and its degradation product aminoacetaldehyde were
seen, in agreement with the JLP1 up-regulation. For Ind_Z,
only sulfate was significantly overrepresented. Specifically
investigating the metabolism of cysteine, methionine and the
methionine salvage pathway did not indicate any marked
changes in the gene expression controlling these processes.
In addition, it was noted that the high-affinity cysteine trans-
porter YCT1 was strongly up-regulated in Ind_B-treated cells
(Kaur and Bachhawat 2007). All in all, both industrial AFA
treatments induced sulfur starvation responses, although the
effects seemed to be more pronounced in Ind_B.
Beside an up-regulation of sulfate assimilation, the Ind_Z
treatment generally contained a larger number of significantly
enriched GO terms among up-regulated genes compared to
Ind_B, indicating a more diverse and global response caused
by the treatment. These GO terms included plasma membrane
organization (GO:0007009), cellular amino acid biosynthetic
process (GO:0008652), and response to stress (GO:0006950).
One of these stress response genes was the third most up-
regulated gene in Ind_Z, HSP12, which encodes a
membrane-bound protein involved in maintaining membrane
organization (Welker et al. 2010). Other stress-responsive
genes were among the most up-regulated genes in Ind_Z
(> 1.5 log
2
fold changes) and included HSP30,BTN2,
DDR2,OPI10,andCWP2.
Among the down-regulated genes in both Ind_B and
Ind_Z, GO terms were strongly enriched for the biosynthesis
of lipids and sterols, in particular ergosterol (GO:0006696).
Reporter metabolites revealed an ergosterol intermediate,
ergosta-5,7,24(28)-trienol, to be underrepresented in both
treatments. Further, the fatty acid synthesis pathway was
down-regulated including ACC1,FA S 1 ,FA S 2 ,andOLE1,
while the elongases ELO1,ELO2, and ELO3 were largely
b
c
a
Fig. 2 The influence of industrial AFA on S. cerevisae CAT-1 fed-batch
fermentation of sugarcane must. aAccumulated CO
2
loss (in g l
1
)over
five fermentation cycles, bAccumulated CO
2
loss (in g l
1
)inthecourse
of the 5th cycle, and cyeast viability at the end of each fermentation
cycle, measured as the percentage of viable cells in a population of viable
and non-viable cells. Figure legend: white fill (control, no AFA added),
gray fill (Ind_B, composed of 150 mg l
1
AF_B and 150 mg l
1
D_B),
black fill (Ind_Z, composed of 150 mg l
1
AF_Z and 150 mg l
1
D_Z).
Values represent the average of triplicate experiments ± standard
deviations
Appl Microbiol Biotechnol (2017) 101:82378248 8243
unchanged (Fig. 4). The activation of fatty acids for beta-
oxidation by FA A 4 was also down-regulated in both treatments.
Reporter metabolite analysis showed that the lipids, palmitoyl-
CoA in Ind_B, and glycerol-3-phosphocholine in Ind_Z were
underrepresented. Palmitoyl-CoA is a thioester, and hence, its
underrepresentation is in agreement with the indications that
the cells were experiencing sulfur starvation and that fatty acid
synthesis and beta-oxidation were down-regulated.
Further, both treatments showed enrichment of the mito-
chondrion term (GO:0005739) among down-regulated genes
as well as several other mitochondrial processes including
mitochondrial inner membrane (GO:0005743), respiratory
chain complex IV (GO:0005751), and for Ind_Z, also mito-
chondrial translation (GO:0032543) and mitochondrial ribo-
somes (GO:0005762/GO:0005763). We also observed that
ferrocytochrome c was underrepresented in both treatments.
Fig. 4 Schematic representation of the affected parts of yeast metabolism
upon exposure to industrial AFA. Color key indicate the transcript fold
changes in the AFA-treated cells relative to the control and arrows can
represent multiple enzymatic reactions. Samples were collected 4 h after
initiation of the last fermentation cycle
ab
c
Fig. 3 Overview of transcriptional pertubations caused by the AFA
treatments. aPrincipal component analysis (PCA) of normalized tran-
script levels in the three replicates of each experimental condition.
Samples were collected 4 h after initiation of the last fermentation cycle.
bSignificantly differentially expressed up-regulated and cdown-
regulated genes caused by the two AFA treatments relative to the control.
Figure legend: white fill (control, no AFA added), gray fill (Ind_B, com-
posed of 150 mg l
1
AF_B and 150 mg l
1
D_B), black fill (Ind_Z,
composed of 150 mg l
1
AF_Z and 150 mg l
1
D_Z)
8244 Appl Microbiol Biotechnol (2017) 101:82378248
Although the main transcriptional perturbations were sim-
ilar in the two industrial treatments, one opposite regulation
was seen within cellular amino acid biosynthetic process
(GO:0008652). In Ind_B, in particular, branched chain amino
acids and lysine biosynthetic genes were down-regulated,
while these pathways were unaffected or slightly up-
regulated in the Ind_Z-treated cells (Fig. 4).
To determine the transcriptional responses to the physio-
logical phenotypes, linear models were constructed to corre-
late viability and the expression levels of each gene in all
replicates of each treatment. We found a total of 245 genes
with significant positive correlation and 293 genes with sig-
nificant inverse correlation to viability (p< 0.01), and these
gene sets were investigated with an enrichment analysis using
GO terms. The most enriched GO terms were seen among the
genes with negative correlation to viability, meaning that they
were higher expressed in the cultivation with low viability,
and included the biosynthetic processes of hydrogen sulfide,
cysteine, and methionine, in agreement with the observations
in the gene set analysis above. Few GO terms were enriched
among the genes with positive correlation to viability and they
were related to mitochondrial organization and mitochondrial
RNA metabolic processes. This observation agreed with the
gene set analysis and confirmed that mitochondrial processes
were repressed in the low viability/increased stress AFA
treatments.
Discussion
During microbial fermentations, AFA are commonly added to
prevent the detrimental effects of foam formation, such as loss
of fermentor space, leading to increased production costs.
AFA have previously been shown to alter growth characteris-
tics of microorganisms, but only a limited number of studies
have systematically investigated this. Current knowledge on
the topic suggests that the biological effects of AFA exposure
is highly dependent on AFA composition, organism of choice,
and culture conditions (Routledge et al. 2014). Further, it has
been shown that different AFA can lead to both increased and
reduced growth rates of yeasts (Holmes et al. 2006) and that
the yield of recombinant protein in Pichia pastoris and
Eschericha coli can be increased upon AFA exposure (Koch
et al. 1995; Routledge and Bill 2012). Despite it being docu-
mented that AFA can give rise to negative fermentation per-
formance, no studies have investigated the impact of this un-
der industrial relevant conditions or by using genome-wide
transcriptional profiling.
In the present work, we have shown that the growth rate of
industrial S. cerevisiae strain CAT-1 is reduced upon exposure
to AFA that are used in the Brazilian ethanol industry. In
microplate fermentations, at 60 mg l
1
, corresponding to the
average AFA concentration used in industrial settings
(Pecege-Esalq 2012), we observed a 46% reduction in
growth rates upon exposure to currently used industrial
defoaming treatments (Ind_B and Ind_Z), and the response
intensified with increasing concentrations. Microplate fermen-
tations with addition of commercially available AFA, fre-
quently used in the fermentation literature (Antifoam C,
Sigma 204, and P2000), resulted in no change in growth rates
at concentrations up to 375 mg l
1
, approximately ten times
the concentration of the industrial AFA tested here. Although
the amount of AFA utilized in scientific publications is rarely
stated, this correspond to concentration higher than what is
commonly used for defoaming purposes in publications where
this is explicit (Brochado et al. 2010) as well as the manufac-
turers recommendations. These findings suggest that indus-
trially used AFA contain components more harmful to yeasts
than what is used in commercially available AFA. One expla-
nation for this might be that industrially used AFA are of
varying quality due to efforts to keep production costs down.
To further investigate how the physiological perturbations
observed in laboratory fermentations would translate into the
industrial ethanol production process, we carried out labora-
tory simulations of the Brazilian sugarcane-based ethanol pro-
duction process with recycling of the yeast biomass for 5 cy-
cles (usually done for 200230 days in industry, with 2 cycles
per day). For these fermentations, we used AFA concentra-
tions of five times the average concentration used in industrial
settings, in order to make the effects more pronounced. Since
the day to day variation in AFA usage in the industry can
change up to tenfold, this can be considered as a plausible
exposure level under industrial conditions. The physiological
effects of the treatments showed a reduction in viability
exerted by both treatments after five fermentation cycles.
This led to a decrease in the CO
2
evolution which can be used
as a proxy for the ethanol production and hence suggests a
decrease in ethanol productivity. Since the Brazilian ethanol
production process is already extremely efficient with yields
as high as 93% of the theoretical maximum (Amorim et al.
2011) and that more than half of the ethanol final costs are due
to the costs of raw materials, any increase in efficiency would
result in major economic gains (Della-Bianca et al. 2013).
Taken together, the data suggests that industrial AFA is a
potential stress factor to yeast cells in sugarcane fermentation,
in addition to the commonly reported ones, such as thermal,
osmotic and ethanolic stresses (Basso et al. 2008; Amorim
et al. 2011; Della-Bianca et al. 2013).
At the transcriptional level, several of the affected cellu-
lar processes were seen in both treatments, despite Ind_Z giv-
ing rise to the most pronounced negative physiological phe-
notype. Interestingly, both treatments showed indications of
sulfur starvation, which is surprising considering the sulfur
availability from the sulfuric acid added for lowering the pH
during recycling. Additionally, a down-regulation of mito-
chondrial processes was observed, which suggests that
Appl Microbiol Biotechnol (2017) 101:82378248 8245
oxygen availability is reduced, as previously shown to be
caused by AFA addition to culture medium (Koch et al.
1995). The Ind_Z treatment resulted in about half the viability
of the control and showed a much more diverse and global
transcriptional responses, including a stronger down-
regulation of mitochondrial processes and an up-regulation
of several stress-response genes which were not seen in
Ind_B.
Lipid metabolism proved to be strongly affected in both
treatments, in particular sterol and fatty acid synthesis, which
was down-regulated with respect to the control. Previous work
has shown that sterol abundance increased in Kluyveromyces
bulgaricus and Saccharomyces uvarum under exposure to
polyoxyalkylene glycol-oleic acid condensates in aerobic cul-
tivations. The relative composition of sterols was unaffected
in S. uvarum,whileinK. bulgaricus, there was an increase in
ergosterol ratio (Pawiroharsono et al. 1987). The authors fur-
ther stated that the permeability to certain sugars relevant for
molasses fermentation, such as glucose and sucrose, was af-
fected by the presence of the AFA investigated. In the present
study, under microaerobic conditions, we saw that the ergos-
terol biosynthetic pathway was down-regulated while only
transcript levels of ERG24 and ERG4 were unchanged (Fig.
4). More recently, it was reported that S. cerevisiae grown
under ethanol stress had elevated sterol levels while ERG
genes were down-regulated (Lahtvee et al. 2016), and empha-
sizes that the overall sterol content cannot be inferred based on
transcript levels alone. Another study showed that oleic acid
gets incorporated into phospholipids and glycolipids, and that
AFA increased the overall lipid content in the filamentous
fungus Aspergillus niger (Nemec and Jernejc 2002). Thus,
alterations in lipid biosynthesis may alter membrane fluidity
(Lahtvee et al. 2016) and therefore increase sensitivity of yeast
cells toward stress factors normally found in molasses-based
fermentations (Della-Bianca et al. 2013).
Taken together, the observed reduction in growth rate and
cell viability, as well as increased stress responses, could be
explained by several factors. The repression of sterol metabo-
lism suggests a change in sterol composition, likely as a re-
sponse to an increased membrane permeability as previously
reported for yeast upon AFA exposure (Pawiroharsono et al.
1987). In inhibitor-rich medium such as molasses (Della-
Bianca et al. 2013), this increased permeability could be con-
tributing to the reduced growth rate since the cells attempt to
cope with inhibitors by inducing stress responses, e.g., to
maintain membrane organization as seen in the up-regulation
of HSP12 in this study. This requires an increased ATP de-
mand which is then diverted away from growth processes. In
turn, failing to efficiently remove inhibitors might contribute
to the observed decrease in viability as well. Another factor is
the oxygen transfer rate in the medium, which can be signif-
icantly increased or reduced upon AFA addition (Morao et al.
1999; Routledge et al. 2011).Theobserveddown-regulation
of mitochondrial processes suggests a reduction in oxygen
availability and this could contribute to a reduced ATP gener-
ation via oxidative phosphorylation and hence hamper
growth. In particular, since the scale-down molasses fermen-
tations conducted in this study took place under microaerobic
conditions, changes in dissolved oxygen concentrations due to
the presence of AFA might have drastic effects on cell
physiology.
Overall, this study has documented distinct negative effects
associated with the use of industrial AFA currently used in the
Brazilian ethanol fermentation industry. The perturbations
were documented at physiological and transcriptional levels.
Our results suggest that the decreased viability and induction
of stress responses is strongly related to changes in lipid me-
tabolism. These findings highlight the importance in develop-
ing less harmful defoaming strategies in industrial fermenta-
tions, in order to prevent compromising cellular performance
of the production organism.
Acknowledgements The authors would like to thank Dr. Tammy Doty
for extraction of total RNA and Camila do Nascimento and João
Rodenbusch Destro for their helpful support in carrying out HPLC anal-
ysis and assistance in cultivation experiments. This study was funded by
Novozymes Latin America Ltda. (Araucária, Brazil) and Fundação de
Amparo à Pesquisa do Estado de São Paulo (FAPESP) (grant number
2016/10240-7). We are also grateful to Victor Guadalupe Medina, Vijay
Raghavendran, Viviane Müller and Daniel Cardinali for carefully reading
this manuscript and giving valuable comments.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Ethical approval This article does not contain any studies with human
participants or animals performed by any of the authors.
References
Amorim HV, Lopes ML, de Castro Oliveira JV, Buckeridge MS,
Goldman GH (2011) Scientific challenges of bioethanol production
in Brazil. Appl Microbiol Biotechnol 91:12671275. https://doi.org/
10.1007/s00253-011-3437-6
Anders S, Pyl PT, Huber W (2015) HTSeqa Python framework to work
with high-throughput sequencing data. Bioinformatics 31:166169.
https://doi.org/10.1093/bioinformatics/btu638
Andrews S (2012) FastQC a quality control tool for high throughput
sequence data. http://www.bioinformatics.babraham.ac.uk/projects/
fastqc. Accessed 6 Sep 2017
Aung HW, Henry SA, Walker LP (2013) Revising the representation of
fatty acid, glycerolipid, and glycerophospholipid metabolism in the
consensus model of yeast metabolism. Ind Biotechnol 9:215228.
https://doi.org/10.1089/ind.2013.0013
Babrzadeh F, Jalili R, Wang C, Shokralla S, Pierce S, Robinson-
Mosher A, Nyren P, Shafer RW, Basso LC, de Amorim HV,
de Oliveira AJ, Davis RW, Ronaghi M, Gharizadeh B,
Stambuk BU (2012) Whole-genome sequencing of the effi-
cient industrial fuel-ethanol fermentative Saccharomyces
8246 Appl Microbiol Biotechnol (2017) 101:82378248
cerevisiae strain CAT-1. Mol Gen Genomics 287:485494.
https://doi.org/10.1007/s00438-012-0695-7
Basso LC, de Amorim HV, de Oliveira AJ, Lopes ML (2008) Yeast
selection for fuel ethanol production in Brazil. FEMS Yeast Res 8:
11551163. https://doi.org/10.1111/j.1567-1364.2008.00428.x
Basso TO, de Kok S, Dario M, do Espirito-Santo JCA, Müller G, Schlölg
PS, Silva CP, Tonso A, Daran J-M, Gombert AK, van Maris AJA,
Pronk JT, Stambuk BU (2011) Engineeringtopology and kinetics of
sucrose metabolism in Saccharomyces cerevisiae for improved eth-
anol yield. Metab Eng 13:694703. https://doi.org/10.1016/j.
ymben.2011.09.005
Basso TO, Gomes FS, Lopes ML, de Amorim HV, Eggleston G, Basso
LC (2014) Homo- and heterofermentative lactobacilli differently
affect sugarcane-based fuel ethanol fermentation. Antonie Van
Leeuwenhoek 105:169177. https://doi.org/10.1007/s10482-013-
0063-6
Blasco L, Viñas M, Villa TG (2011) Proteins influencing foam formation
in wine and beer: the role of yeast. Int Microbiol 14:6171. https://
doi.org/10.2436/IM.V14I2.54426
Brochado AR, Matos C, Møller BL, Hansen J, Mortensen UH, Patil KR
(2010) Improved vanillin production in bakers yeast through in
silico design. Microb Cell Factories. https://doi.org/10.1186/1475-
2859-9-84
Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S, the
AmiGO Hub, the Web Presence Working Group (2009) AmiGO:
online access to ontology and annotation data. Bioinformatics 25:
288289. https://doi.org/10.1093/bioinformatics/btn615
Della-Bianca E, Basso TO, Stambuk BU, Basso LC, Gombert AK (2013)
What do we know about the yeast strains from the Brazilian fuel
ethanol industry? Appl Microbiol Biotechnol 97:979991. https://
doi.org/10.1007/s00253-012-4631-x
Eisner MD, Jeelani SAK, Bernhard L, Windhab EJ (2007) Stability of
foams containing proteins, fat particles and nonionic surfactants.
Chem Eng Sci 62:19741987. https://doi.org/10.1016/j.ces.2006.
12.056
Engel SR, Dietrich FS, Fisk DG, Binkley G, Balakrishnan R, Costanzo
MC, Dwight SS, Hitz BC, Karra K, Nash RS, Weng S, Wong ED,
Lloyd P, Skrzypek MS, Miyasato SR, SimisonM, Cherry JM (2014)
The reference genome sequence of Saccharomyces cerevisiae:then
and now. G3 4:389398. https://doi.org/10.1534/g3.113.008995
Garcia-Ochoa F, Gomez E (2009) Bioreactor scale-up and oxygen trans-
fer rate in microbial processes: an overview. Biotechnol Adv 27:
153176. https://doi.org/10.1016/j.biotechadv.2008.10.006
Gierest H, Thao NN, Surdin-Kerjan Y (1985) Transcriptional regulation
of the MET3 gene of Saccharomyces cerevisiae.Gene34:269281.
https://doi.org/10.1016/0378-1119(85)90136-2
Grosz R, Stephanopoulos G (1990) Physiological, biochemical, and
mathematical studies of micro-aerobic continuous ethanol fermen-
tation by Saccharomyces cerevisiae. I: hysteresis, oscillations, and
maximum specific ethanol productivities in chemostat culture.
Biotechnol Bioeng 36:10061019. https://doi.org/10.1002/bit.
260361006
Hogan DA, Auchtung TA, Hausinger RP (1999) Cloning and character-
ization of a sulfonate/α-ketoglutarate dioxygenase from
Saccharomyces cerevisiae. J Bacteriol 181:58765879
Holmes W, Smith R, Bill R (2006) Evaluationof antifoams in the expres-
sion of a recombinant FC fusion protein in shake flask cultures of
Saccharomyces cerevisiae.Microb Cell Factories 5:P30. https://doi.
org/10.1186/1475-2859-5-S1-P30
Junker B (2007) Foam and its mitigation in fermentation systems.
Biotechnol Prog 23:767784. https://doi.org/10.1021/bp070032r
Kaur J, Bachhawat AK (2007) Yct1p, a novel, high-affinity, cysteine-
specific transporter from the yeast Saccharomyces cerevisiae.
Genetics 176:877890. https://doi.org/10.1534/genetics.107.
070342
Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013)
TopHat2: accurate alignment of transcriptomes in the presence of
insertions, deletions and gene fusions. Genome Biol 14:R36. https://
doi.org/10.1186/gb-2013-14-4-r36
Koch V, Riiffer H, Schiigerl K, Innertsberger E, Menzel H, Weis J (1995)
Effect of antifoam agents on the medium and microbial cell proper-
ties and process performance in small and large reactors. Process
Biochem 30:435446
Lahtvee P-J, Kumar R, Hallstrom BM, Nielsen J (2016) Adaptation to
different types of stress convergeon mitochondrial metabolism. Mol
Biol Cell 27:25052514. https://doi.org/10.1091/mbc.E16-03-0187
Love MI, Huber W, Anders S, Lönnstedt I, Speed T, Robinson M, Smyth
G, McCarthy D, Chen Y, Smyth G, Anders S, Huber W, Zhou Y-H,
Xia K, Wright F, Wu H, Wang C, Wu Z, Hardcastle T, Kelly K, Van
De Wiel M, Leday G, Pardo L, Rue H, Van Der Vaart A, Van
Wieringen W, Boer J, Huber W, Sültmann H, Wilmer F, von
Heydebreck A, Haas S, Korn B, Gunawan B, Vente A, Füzesi L,
Vingron M, Poustka A, Gentleman R, Carey V, Bates D, Bolstad B,
Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K,
Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler
M, Rossini A, Sawitzki G, Smith C, Smyth G, Tierney L, Yang J,
Zhang J, McCullagh P, Nelder J, Hansen K, Irizarry R, Wu Z, Risso
D, Schwartz K, Sherlock G, Dudoit S, Smyth G, Bottomly D, Walter
N, Hunter J, Darakjian P, Kawane S, BuckK, Searles R, Mooney M,
McWeeney S, Hitzemann R, Pickrell J, Marioni J, Pai A, Degner J,
Engelhardt B, Nkadori E, Veyrieras J-B, Stephens M, Gilad Y,
Pritchard J, Hastie T, Tibshirani R, Friedman J, Bi Y, Davuluri R,
Feng J, Meyer C, Wang Q, Liu J, Liu X, Zhang Y, Benjamini Y,
Hochberg Y, Bourgon R, Gentleman R, Huber W, McCarthy D,
Smyth G, Li J, Tibshirani R, Cook R, Hammer P, Banck M,
Amberg R, Wang C, Petznick G, Luo S, Khrebtukova I, Schroth
G, Beyerlein P, Beutler A, Frazee A, Langmead B, Leek J, Trapnell
C, Hendrickson D, Sauvageau M, Goff L, Rinn J, Pachter L, Glaus
P, Honkela A, Rattray M, Anders S, Reyes A, Huber W, Sammeth
M, Robinson M, McCarthy D, Smyth G, Zhou X, Lindsay H,
Robinson M, Leng N, Dawson J, Thomson J, Ruotti V, Rissman
A, Smits B, Haag J, Gould M, Stewart R, Kendziorski C, Law C,
Chen Y, Shi W, Smyth G, Hubert L, Arabie P, Witten D, Irizarry R,
Wu Z, Jaffee H, Asangani I, Dommeti V, Wang X, Malik R, Cieslik
M, Yang R, Escara-Wilke J, Wilder-Romans K, Dhanireddy S,
Engelke C, Iyer M, Jing X, Wu Y-M, Cao X, Qin Z, Wang S,
Feng F, Chinnaiyan A, Ross-Innes C, Stark R, Teschendorff A,
Holmes K, Ali H, Dunning M, Brown G, Gojis O, Ellis I, Green
A, Ali S, Chin S-F, Palmieri C, Caldas C, Carroll J, Robinson D,
Chen W, Storey J, Gresham D, McMurdie P, Holmes S, Vasquez J,
Hon C, Vanselow J, Schlosser A, Siegel T, Zhou Y, Zhu S, Cai C,
Yuan P, Li C, Huang Y, Wei W, Cox D, Reid N, Robinson M, Smyth
G, Pawitan Y, Armijo L, Di Y, Schafer D, Cumbie J, Chang J,
Abramowitz M, Stegun I, Newton M, Kendziorski C, Richmond
C, Blattner F, Tsui K, Huber W, von Heydebreck A, Sultmann H,
Poustka A, Vingron M, Durbin B, Hardin J, Hawkins D, Rocke D,
Friedman J, Hastie T, Tibshirani R, Cule E, Vineis P, De Iorio M,
Cook R, Weisberg S, Lawrence M, Huber W, Pagès H, Aboyoun P,
Carlson M, Gentleman R, Morgan M, Carey V, Anders S, Pyl P,
Huber W, Delhomme N, Padioleau I, Furlong E, Steinmetz L, Liao
Y, Smyth G, Shi W, Kim D, Pertea G, Trapnell C, Pimentel H,
Kelley R, Salzberg S (2014) Moderated estimation of fold change
and dispersion for RNA-seq data with DESeq2. Genome Biol 15:
550. https://doi.org/10.1186/s13059-014-0550-8
Morao A, Maia CI, Fonseca MMR, Vasconcelos JMT, Alves SS (1999)
Effect of antifoam addition on gas-liquid mass transfer in stirred
fermenters. Bioprocess Eng 20:165172
Nemec T, Jernejc K (2002) Influence of Tween 80 on lipid metabolism of
an Aspergillus niger strain. Appl Biochem Biotechnol 101:229238.
https://doi.org/10.1385/ABAB:101:3:229
Appl Microbiol Biotechnol (2017) 101:82378248 8247
Patil KR, Nielsen J (2005) Uncovering transcriptional regulation of me-
tabolism by using metabolic network topology. Proc Natl Acad Sci
USA102:26852689. https://doi.org/10.1073/pnas.0406811102
Pawiroharsono S, Naji B, Bonaly R, Tonetti F, Chasseboeuf C, Richter JP
(1987) Permeability and membrane sterol distribution in
Saccharomyces uvarum and Kluyveromyces bulgaricus grown in
presence of polyoxyalkylene glycol-oleic acid condensates. Appl
Microbiol Biotechnol 27:181185. https://doi.org/10.1007/
BF00251942
Pecege-Esalq (2012) Custos de producao de cana-de-acucar, acucar e
etanol no Brasil: acompanhamento da Safra 2011/2012 - Centro-
sul http://canaldoprodutorcombr/sites/default/files/relatorio_
Custos_Prod_Cana_2011_12pdf Accessed 6 Sep 2017
Prins A, Van Riet K (1987) Proteins and surface effects in fermentation:
foam antifoam and mass transfer. Trends Biotechnol 5:296301
Raghavendran V, Basso TP, da Silva JB, Basso LC, Gombert AK (2017)
A simple scaled down system to mimic the industrial production of
first generation fuel ethanol in Brazil. Antonie Van Leeuwenhoek
110:971983. https://doi.org/10.1007/s10482-017-0868-9
Renewable Fuels Association (2016) Fueling a high octane future. https://
wwwethanolrfaorg/wp-content/uploads/2016/02/Ethanol-Industry-
Outlook-2016pdf. Accessed 6 Sep 2017
Routledge SJ, Bill RM (2012) The effect of antifoam addition on protein
production yields. In: Bill RM (ed) Recombinant Protein Production
in Yeast. Springer, Totowa, pp 8797
Routledge SJ, Hewitt CJ, Bora N, Bill RM (2011) Antifoam addition to
shake flask cultures of recombinant Pichia pastoris increases yield.
Microb Cell Factories. https://doi.org/10.1186/1475-2859-10-17
Routledge SJ, Poyner DR, Bill RM (2014) Antifoams: the overlooked
additive? Pharm Bioprocess 2:103106
Varemo L, Nielsen J, Nookaew I (2013) Enriching the gene set analysis of
genome-wide data by incorporating directionality of gene expres-
sion and combining statistical hypotheses and methods. Nucleic
Acids Res 41:43784391. https://doi.org/10.1093/nar/gkt111
Vidyarthi AS, Desrosiers M, Tyagi RD, Valéro JR (2000) Foam control in
biopesticide production from sewage sludge. J Ind Microbiol
Biotechnol 25:8692. https://doi.org/10.1038/sj.jim.7000038
Walker GM (2010) Bioethanol: science and technology of fuel alcohol.
Ventus Publishing ApS, Frederiksberg
Welker S, Rudolph B, Frenzel E, Hagn F, Liebisch G, Schmitz G,
Scheuring J, Kerth A, Blume A, Weinkauf S, Haslbeck M, Kessler
H, Buchner J (2010) Hsp12 is an intrinsically unstructured stress
protein that folds upon membrane association and modulates mem-
brane function. Mol Cell 39:507520. https://doi.org/10.1016/j.
molcel.2010.08.001
Yates A, Akanni W, Amode MR, Barrell D, Billis K, Carvalho-Silva D,
Cummins C, Clapham P, Fitzgerald S, Gil L, Girón CG, Gordon L,
Hourlier T, Hunt SE, Janacek SH, JohnsonN, Juettemann T, Keenan
S, Lavidas I, Martin FJ, Maurel T, McLaren W, Murphy DN, Nag R,
Nuhn M, Parker A, Patricio M, Pignatelli M, Rahtz M, Riat HS,
Sheppard D,Taylor K, Thormann A, Vullo A, Wilder SP, Zadissa A,
Birney E, Harrow J, Muffato M, Perry E, Ruffier M, Spudich G,
Trevanion SJ, Cunningham F, Aken BL, Zerbino DR, Flicek P
(2016) Ensembl 2016. Nucleic Acids Res 44:D710D716. https://
doi.org/10.1093/nar/gkv1157
Zhang N, Merlotti C, Wu J, Ismail T, El-Moghazy A-N, Ahmed Khan S,
Butt A, Gardner DCJ, Sims PFG, Oliver SG (2001) Functional
analysis of six novel ORFs on the left arm of chromosome XII of
Saccharomyces cerevisiae reveals three of them responding to S-
starvation. Yeast 18:325334. https://doi.org/10.1002/1097-
0061(20010315)18:4<325::AID-YEA669>3.0.CO;2-K
8248 Appl Microbiol Biotechnol (2017) 101:82378248
... In ethanol production, foam formation normally occurs due to carbon dioxide production as a co-product of ethanol [228,229]. The foam reduces the fermentation tank's working capacity, resulting in higher production costs and lower productivity [230,231]. ...
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Ethanol is a promising biofuel that can replace fossil fuel, mitigate greenhouse gas (GHG) emissions, and represent a renewable building block for biochemical production. Ethanol can be produced from various feedstocks. First‐generation ethanol is mainly produced from sugar‐ and starch‐containing feedstocks. For second‐generation ethanol, lignocellulosic biomass is used as a feedstock. Typically, ethanol production contains four major steps, including the conversion of feedstock, fermentation, ethanol recovery, and ethanol storage. Each feedstock requires different procedures for its conversion to fermentable sugar. Lignocellulosic biomass requires extra pretreatment compared to sugar and starch feedstocks to disrupt the structure and improve enzymatic hydrolysis efficiency. Many pretreatment methods are available such as physical, chemical, physicochemical, and biological methods. However, the greatest concern regarding the pretreatment process is inhibitor formation, which might retard enzymatic hydrolysis and fermentation. The main inhibitors are furan derivatives, aromatic compounds, and organic acids. Actions to minimize the effects of inhibitors, detoxification, changing fermentation strategies, and metabolic engineering can subsequently be conducted. In addition to the inhibitors from pretreatment, chemicals used during the pretreatment and fermentation of byproducts may remain in the final product if they are not removed by ethanol distillation and dehydration. Maintaining the quality of ethanol during storage is another concerning issue. Initial impurities of ethanol being stored and its nature, including hygroscopic, high oxygen and carbon dioxide solubility, influence chemical reactions during the storage period and change ethanol’s characteristics (e.g., water content, ethanol content, acidity, pH, and electrical conductivity). During ethanol storage periods, nitrogen blanketing and corrosion inhibitors can be applied to reduce the quality degradation rate, the selection of which depends on several factors, such as cost and storage duration. This review article sheds light on the techniques of control used in ethanol fuel production, and also includes specific guidelines to control ethanol quality during production and the storage period in orderto preserve ethanol production from first‐generation to second‐generation feedstock. Finally, the understanding of impurity/inhibitor formation and controlled strategies is crucial. These need to be considered when driving higher ethanol blending mandates in the short term, utilizing ethanol as a renewable building block for chemicals, or adopting ethanol as a hydrogen carrier for the long‐term future, as has been recommended.
... Velugula-Yellela et al. 8 use change in the local dissolved oxygen variability to predict foaming; in their study, they observe the impact of antifoam and media selection on cellular health and production. In a similar study, Nielsen et al. 9 show that different concentrations of seven different types of commercial and industrial AFA compromised the growth rates and the glucose uptake rates for an ethanol production process. ...
... In fermentation processes, foam formation is inevitable and occurs as a consequence of the introduction of large masses of air into the process and the presence of colloidal substances or long-chain organic compounds (soluble proteins, alcohols, etc.) [1]. There are different ways to control and/or remove foam, such as the use of mechanical devices, removal of foaming agents from process units that prevent their formation, spreading water over it or breaking down by applying pressured flows over foam [2]. ...
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... In fermentation processes, foam formation is inevitable and occurs as a consequence of the introduction of large masses of air into the process and the presence of colloidal substances or long-chain organic compounds (soluble proteins, alcohols, etc.) [1]. There are different ways to control and/or remove foam, such as the use of mechanical devices, removal of foaming agents from process units that prevent their formation, spreading water over it or breaking down by applying pressured flows over foam [2]. ...
... Another cell-surface phenotype, intensive foam formation, is also of great concern as highly foaming yeast strains do not allow the use of the fermentor total volume capacity, decreasing productivity and increasing costs due to the use of antifoam agents [3,19,26,27]. Moreover, it has been recently shown that industrial antifoam agents impair ethanol fermentation and decrease yeast cell viability, inducing a clear stress response in an industrial strain under conditions that simulate the Brazilian sugarcane-based ethanol production process with cell recycling [28]. While foam formation and stability are important in some fermented beverages like sparkling wines and beer [29,30], excessive foam production during fuel-ethanol fermentation is undesirable. ...
Preprint
Many contaminant yeast strains able to survive inside fuel ethanol industrial vats show detrimental cell surface phenotypes, such as filamentation, invasive growth, flocculation, biofilm formation and excessive foam production. Previous studies have linked some of these phenotypes to the expression of FLO genes, and the presence of gene length polymorphisms causing the expansion of FLO gene size appears to result in stronger flocculation and biofilm formation phenotypes. We have performed here a molecular analysis of FLO1 and FLO11 gene polymorphisms present in contaminant strains of S. cerevisae from Brazilian fuel ethanol distilleries showing strong foaming phenotypes during fermentation. The size variability of these genes was correlated with cellular hydrophobicity, flocculation and highly foaming phenotypes in these yeast strains. Our results also show that deleting the major activator of FLO genes (the FLO8 gene) from the genome of a contaminant and highly foaming industrial strain avoids problematic foam formation, flocculation, invasive growth and biofilm production by the engineered (flo8∆::BleR / flo8Δ::kanMX) yeast strain. Thus, the characterization of highly foaming yeasts and the influence of FLO8 in this phenotype opens new perspectives for yeast strain engineering and optimization in the sugarcane fuel-ethanol industry.
... Apart from the additional cost, anti-foam agents are known to alter yeast physiology. For instance, Nielsen et al. (2017) demonstrated that an industrial anti-foam reduced the cell viability of a starter strain of S. cerevisiae from the bioethanol industry (CAT-1). The effects became more intense with an increase in the concentration of the anti-foam. ...
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... In fact, most of the molecular markers showed increasing trends of correlation with FTIR profiles at increasing ethanol concentrations, confirming the primary role of the FTIR analytical system in finely characterizing the physiological status of cells in various conditions, including stress [18,[49][50][51][52]. Several studies have shown that the transcription of TEF-1α can be induced by increasing stressing condition [53,54]. In addition, FAS-1, one of the main actors in yeast long-chain fatty acid metabolism, was interested in stress response to ethanol [55,56], antifoaming compounds [57] and high temperature [58], whereas the mitochondrial COX gene is a known to be part of the cellular response to different types of stressing agents, such as ethanol or high temperatures [59]. It has also been demonstrated that most of these genes play an important role in the response of yeasts to stressful fermentation conditions [56,60] and oxidative stress [61,62]. ...
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Chapter
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