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\begin{document}

\title{Omics analysis reveals the mechanism of metabolic oscillation during
continuous VHG ethanol fermentation by Saccharomyces cerevisiae}



\author[1]{Xue Zhang}%
\author[2]{Liang Wang}%
\author[3]{Qian Li}%
\author[4]{Riaan Haan}%
\author[5]{Fan Li}%
\author[1]{Chen-Guang Liu}%
\author[1]{Fengwu Bai}%
\affil[1]{Shanghai Jiao Tong University}%
\affil[2]{Dalian Polytechnic University}%
\affil[3]{Dalian University}%
\affil[4]{University of the Western Cape}%
\affil[5]{COFCO Nutrition and Health Research Institute}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\selectlanguage{english}
\begin{abstract}
During continuous very high gravity (VHG) fermentation, yeast cells
exhibit sustained oscillation of residual glucose, ethanol, and biomass,
which remains a fundamental and unanswered question associated with
product inhibition. In this study, the oscillating process was
characterized through transcriptome and metabolome analysis in one
sinusoid cycle. By integrating analysis of 26 metabolites and 90 genes
related to carbon metabolism, the results confirmed that fermentation
oscillation could be attributed to intercellular metabolic oscillation
with phase difference of sinusoidal waveform. Furthermore, expression
changes of stress response genes indicated that dynamic ethanol
inhibition was a primary factor responsible for the oscillation of
metabolism. This study not only contributes to elucidation of the
mechanism of oscillating fermentation through strong product inhibition,
but also provides new understanding of other fermentation processes in
an unsteady state.%
\end{abstract}%



\sloppy


\textbf{Keywords}

\emph{Saccharomyces cerevisiae} ; oscillation; ethanol inhibition;
metabolomics; transcriptomics

\textbf{Introduction}

As the demand for oil further increases, the diversification of fuel
supply through alternative energy sources such as fuel ethanol has
become part of the national energy policy in many countries (Kircher,
2015). Although cellulosic ethanol is widely regarded as a promising
liquid fuel (Liu et al., 2019), a lack of substantive breakthroughs in
core technologies and low economic feasibility is still a major
limitation. This is a result of complex pretreatment requirements,
inhibitory compounds in the fermentation broth, the cost of cellulases,
and inefficient pentose metabolization by the fermentation strain (Han
et al., 2020). In addition, achievable cellulosic ethanol titers
(3\%-6\% v/v) are lower than starch-based ethanol titers (10\%-12\% v/v)
which significantly increases energy cost for distillation and stillage
treatment (Bai, Anderson, \& Moo-Young, 2008).

Very high gravity (VHG) fermentation with [?]25\% w/v substrate improves
ethanol titer ([?]15\% v/v) with concomitant savings in energy consumption
(Puligundla, Smogrovicova, Obulam, \& Ko, 2011). VHG ethanol
fermentation is usually practiced \emph{via} labor-intensive and
time-consuming batch operation, which is not suitable for production at
large scale . On the other hand, continuous VHG ethanol fermentation
(CVEF) could be applied in industry, but often trigger a sustained
oscillation of residual glucose, ethanol, and biomass, leading to an
undesirable unstable process (Bai, Ge, Anderson, \& Moo-Young, 2009).
The toxicity of ethanol to the yeast \emph{S. cerevisiae} is a primary
factor limiting titer and productivity. Ethanol tolerance is related to
the fluidity, structure, and composition of the plasma membrane, and the
levels of unsaturated fatty acids, ergosterol, amino acids, inositol,
heat shock proteins (HSPs), ATPase and the storage carbohydrates (Lam,
Ghaderi, Fink, \& Stephanopoulos, 2014).

Attenuating oscillation has been attempted by engineering strategies.
Employment of three tubular bioreactors in series following a stirred
tank bioreactor packed with intalox ceramic saddle packing enhanced the
yeast tolerance to ethanol through step-by-step adaption and cell
immobilization (Bai, Chen, Anderson, \& Moo-Young, 2004). Gas stripping
with nitrogen or fermentation exhaust gas directly decreased the ethanol
in the broth that help yeast cell to relieve ethanol toxicity (Wang,
Zhao, Xue, \& Bai, 2013). These approaches effectively attenuated
oscillations and achieved quasi-steady-states in ethanol fermentation.

Periodic behavior has been investigated in eukaryotic and prokaryotic
organisms (Panda, 2016). \emph{S. cerevisiae} exhibits various modes of
oscillatory behavior both in cell-free extract and during continuous
culture (Bai et al., 2009; Chin, Marcus, Klevecz, \& Li, 2012) in the
form of glycolytic oscillation with a period of about 1 min, respiratory
oscillation with a period of 40-60 min, and cell cycle oscillation with
a period of 2-45 h (Richard, 2003). The glycolytic oscillation is
mediated by glycolytic intermediates, and the allosteric properties of
phosphofructokinase (PFK) and the positive feedback exerted on it were
assumed to be responsible for periodic operation (Gustavsson et al.,
2014; Olsen, Stock, \& Bagatolli, 2020). The respiratory oscillation,
monitored by dissolved oxygen, oxygen uptake rate (OUR), and carbon
dioxide evolution rate (CER), was modulated by sulfate assimilation,
ethanol degradation and respiration (Patnaik, 2003; Tu et al., 2007).
The cell cycle oscillations, synchronized with the cell division cycle,
were represented by extracellular variables (OUR, CER, glucose, ethanol,
and biomass), intracellular variables (storage carbohydrates), and cell
cycle related variables (budding index and cell size distribution)
(Ewald, Kuehne, Zamboni, \& Skotheim, 2016).

Although dynamic ethanol inhibition has been proved to be a key factor
in the induction of oscillatory behavior (Wang et al., 2013), the detail
of yeast cells response remains a fundamental and unanswered question.
In this study, a systematic approach via transcriptomic and metabolomic
analysis was carried out, with particularly focus on the cellular events
of \emph{S. cerevisiae} under CVEF and cellular metabolism affected by
the fermentation parameters. A way to comprehend an unsteady state of
fermentation in such a complex metabolic oscillation is demonstrated.

\textbf{Materials and Methods}

\textbf{Strain, medium, and chemicals}

All experiments were performed with an industrial yeast strain \emph{S.
cerevisiae} 4126. Yeast extract peptone dextrose (YPD) medium composed
of 5 g/L yeast extract powder, 3 g/L peptone (Sangon Biotech, Shanghai)
and different levels of glucose (30, 120, 280 g/L) was used. YPD medium
containing 30 g/L and 120 g/L glucose for pre-cultures in flasks and
fermenters was sterilized by the usual method (121\selectlanguage{ngerman}°C, 20 min), while the
YPD medium composed of 280 g/L glucose for continuous ethanol
fermentation was sterilized at 110°C for 15 min to minimize the Maillard
reaction between amino acids and reducing sugars. YPD medium containing
of 120 g/L and 280 g/L glucose was perceived as low gravity medium and
very high gravity (VHG) medium, respectively. The following metabolites:
GLC, G6P, F6P, FBP, 6PG, R5P, T6P, TRE, GAP, GOLP, 2+3PG, PEP, PYR, OAA,
CIT, AKG, SUC, FUM, MAL, ATP, ADP, AMP, NAD\textsuperscript{+}, NADH,
NADP\textsuperscript{+}, NADPH, AcCoA (refer to the list of
abbreviations) used as standards for LC-MS/MS analysis were purchased
from Sigma-Aldrich.

\textbf{Pre-culture and continuous VHG ethanol fermentation}

Pre-cultures in 250 mL shake-flasks and 2.5-L laboratory stirred tank
reactor (KoBioTech, Korea) with a working volume of 1.7 L were cultured
at 30°C and 300 rpm. The pH and aeration rate were controlled at 4.50
(adjusted with 2 mol/L sterilized NaOH) and 0.05 vvm for the bioreactor.
When residual glucose in the pre-culture was below 1.0 g/L, CVEF
operation was initiated at the same conditions as above. The VHG medium
was fed for continuous ethanol fermentation at a dilution rate of 0.027
h\textsuperscript{-1}.

\textbf{Fermentation parameters determination}

Fermentation parameters containing the concentrations of residual
glucose, ethanol, glycerol, succinate, biomass, cell viability, and
oxidation reduction potential (ORP) were investigated to assess the CVEF
system.

Triplicate samples were withdrawn and centrifuged at 10,000 rpm at
ambient temperature for 5 min. The cell pellet was collected for biomass
determination, and the supernatant was frozen at \selectlanguage{english}-20 degC for HPLC
analysis. An HPLC system with RI-detector (Waters 410, Waters, USA) and
an Aminex HPX 87-H column (300 x7.8 mm, Bio-Rad Laboratories, Hercules,
CA, USA) was used to analyze glucose, ethanol, glycerol, and succinate
in the effluent according to Wang et al.(2013). Biomass was measured as
dry cell weight (DCW), wash the cell pellet obtained previously twice
with deionized water, overnight at 80degC to constant weight, and weigh
the DCW. Cell viability was evaluated \emph{via} methylene blue stain
described by Bai et al. (2004). Living cells were calculated from
biomassxviability. ORP value was monitored by an ORP electrode.

\textbf{Sampling and extraction for extracellular metabolites and total
RNA}

For the sampling of total RNA and extracellular metabolites in yeast
cells, approximately 5-mL sample per time interval was rapidly withdrawn
from the fermenter. Cell pellets were immediately harvested by
centrifugation at ambient temperature and 10,000 rpm for 2 min, washed
with 0.9\% (w/v) NaCl, centrifuged at ambient temperature and 10,000 rpm
for 2 min, and then frozen at --80degC for further analysis. The
supernatant was frozen at --20degC for determination of extracellular
metabolites.

Total RNA was extracted by the RNeasy Mini Kit (Qiagen, Germany). RNA
quality was confirmed by A\textsubscript{260}/A\textsubscript{280} ratio
(\textgreater{}2.0) and agarose gel electrophoresis (28S:18S
ratio=1.5-2.0). RNA samples were dissolved in Milli-Q water and frozen
at --80degC for transcriptome analysis.

\textbf{Transcriptomic analysis}

High-resolution genome-wide transcriptional analysis with RNA-Seq was
carried out by Beijing Genomics Institute (BGI-Huada, Shenzhen, China).
Raw reads were filtered to remove some adapters and low-quality reads,
and the remaining reads were mapped to the reference genome
S288c\_reference\_genome\_R6-1-1\_20110203 to get the transcripts
information. Next, the expression levels of the differentially expressed
genes (DEGs) were normalized by RPKM, defined as the number of reads per
kilobase per million mapped reads. To determine the DEGs in different
periods, absolute value of log\textsubscript{2}-fold changes larger than
1 and false discovery rate lower than 0.01 were selected as screening
criteria.

\textbf{Confirmation of transcriptomic results by qRT-PCR}

To determine the copy numbers of the transcripts, quantitative real-time
PCR (qRT-PCR) was conducted for each sample. Six representative
genes,\emph{ZPS1} (zinc regulatory proteins), \emph{DSE1} (cell
division),\emph{HPF1} (mannose glycoprotein), \emph{EGT2} (cell
division),\emph{TIS11} (iron regulatory proteins) and, \emph{GCV1}
(synthesis of coenzyme factors) were selected to perform the
verification. The primers are shown in Table S1.

\textbf{Quenching and extraction of intracellular metabolites}

Approximately 5 mL sample was rapidly taken from fermenter and injected
into 25 mL precooled (--40degC) 60\% (v/v) methanol solution to interrupt
the cell metabolism as soon as possible, resulting in a final
concentration of 50\% (v/v) methanol. The quenching process lasted 3
min. Cell pellets were then separated using a fast vacuum dependent
filtration (0.45 \selectlanguage{greek}μ\selectlanguage{english}m nominal pore size) and washed with 5 mL precooled
(--40\selectlanguage{ngerman}°C) 50\% (v/v) methanol solution to remove the residual medium
component on the cell surface. Next the filter adhered with cell pellet
was frozen by liquid nitrogen, and rapidly transferred into 9.5 mL
precooled (--40°C) extraction solution composed of 2.5 mL methanol, 5 mL
chloroform, and 2 mL fresh Tricine-EDTA buffer solution (3 mmol/L
Tricine and 3 mmol/L EDTA, pH 7.0, stored at 4°C). The mixture was
vigorously vibrated for 60 min at --40±5°C (dry ice-ethanol bath). The
first aqueous phase was collected by a centrifugation step (12,000 rpm,
5 min, --19°C), more methanol and Tricine-EDTA buffer solution was added
in for a second extraction, and the second aqueous phase was collected
and pooled with the first one. The aqueous phase was stored at \selectlanguage{english}-80degC and
evaporated to dryness before use. The dry residue was reconstituted in
100 \selectlanguage{greek}μ\selectlanguage{english}L of Mill-Q water for LC-MS/MS analysis.

\textbf{LC--MS/MS analysis}

LC instrumentation included a high-performance liquid chromatography
integrated system (Thermo Fisher Scientific Inc., Waltham, MA, USA) with
a XBridge BEH Amide column (2.5 \selectlanguage{greek}μ\selectlanguage{english}m, 2.1\selectlanguage{ngerman}×100 mm, Waters) at 25°C. MS
experiments were performed using a triple quadrupole mass spectrometer
TSQ Quantum UltraTM (Thermo Fisher Scientific Inc., Waltham, MA, USA).
The auto-sampler temperature was kept at 4°C. 5 \selectlanguage{greek}μ\selectlanguage{english}L of sample was
injected with the full loop mode into the system. The XBridge BEH Amide
column was used under acidic conditions, and the mobile phase consisted
of two eluents: eluent A was 20 mmol/L NH\textsubscript{4}Ac in Milli-Q
water, pH 6.5 and eluent B was 100\% acetonitrile. A gradient was run at
a flow rate of 0.3 mL/min; it started with 5\% A, which was increased
linearly to 60\% A in 20 min and stayed at 60\% A for 5 min, then
decreased to 5\% A in 10 min. An equilibration time of 15 min was
allowed before the next injection.

Ionization was performed using an electrospray ionization source (ESI)
operating in the positive and negative ionization modes. Sheath and
auxiliary gas was nitrogen. The collision gas was argon. Xcalibur 2.2
software (Thermo Fisher Scientific Inc.) was used for instrumental
control and data acquisition. Eighteen compounds presented greater
ionization efficiency in negative than in positive ionization mode
(namely G6P, F6P, FBP, 6PG, R5P, T6P, TRE, GAP, GOLP, 2+3PG, PEP, PYR,
OAA, CIT, AKG, SUC, FUM and MAL), while for eight compounds the positive
mode improved ionization efficiency (namely ATP, ADP, AMP,
NAD\textsuperscript{+}, NADH, NADP\textsuperscript{+}, NADPH and AcCoA).
Selected reaction monitoring mode was set, and the collision energy for
an effective fragmentation of the selected precursor ions was optimized
according to the analyte. The spray voltage, vaporizer temperature,
sheath gas pressure, collision pressure, and aux gas pressure were 2.5
kV, 250 \selectlanguage{ngerman}°C, 35 psi, 1.5 psi, and 10 psi, respectively.

\textbf{Results}

\textbf{Physiology of \emph{S. cerevisiae} in continuous VHG ethanol
fermentation}

During CVEF, oscillatory behavior of fermentation parameters, such as
biomass, cell viability, ethanol, residual glucose, and glycerol, are
displayed periodically and sustained with a period of approximately 150
h at the dilution rate of 0.027 h\textsuperscript{-1} (Bai, Chen,
Anderson, et al., 2004; Wang et al., 2013). In order to focus on the
intracellular regulation mechanism of yeast cells during the oscillatory
process, a complete representative period was shown in Figure 1, in
which ethanol, glycerol, biomass, cell viability and living cells
exhibited the similar changing tendencies, while residual glucose
presented the opposite profile.

Considering the periodic wave characteristics, the different phases of
sinusoidal function (0\selectlanguage{greek}π, π/\selectlanguage{english}2, \selectlanguage{greek}π, \selectlanguage{english}and 3\selectlanguage{greek}π/\selectlanguage{english}2) are appropriately applied to
describe the changing trends of parameters for better exploring and
comprehending their correlations (Figure S1). The oscillation curve of
residual glucose is configured as a 0\selectlanguage{greek}π \selectlanguage{english}phase, so the profiles of ethanol
and glycerol are correspondingly considered as a \selectlanguage{greek}π \selectlanguage{english}phase. Because
ethanol as the major product requires the consumption of glucose in
proportion, it naturally follows that there is an opposite tendency
between them. The parameters associated with cell growth (biomass, cell
viability, and living cells) are also classified as a \selectlanguage{greek}π \selectlanguage{english}phase based on
the foundational knowledge that cell growth couples with the generation
of primary metabolites such as ethanol. However, the changes of cell
growth and viability happened prior to other parameters, reflecting a
delayed response of intracellular metabolites. In addition, both high
glucose and ethanol caused a synergy inhibition on viability. According
to the biomass curve, five representative sampling points (P1-5 in
Figure 1) were selected for subsequent metabolic profile analysis and
transcriptomic analysis.

\textbf{Statistical analysis of DEGs}

RNA transcription data was integrated and intercompared in Figure 2. It
is obvious that cellular transcriptional response of yeast cells at P1
was consistent with that at P5 - only 10 DEGs detected, which suggest
that yeast cells exhibit similar transcriptional characteristics in the
same position of different oscillation periods. This was expected as the
end point of one oscillation represents the start point of the following
period. It also confirmed the periodic nature of continuous oscillation.

The big DEGs counts between the neighboring points showed the sustained
and significant transcriptional changes attributed to the unsteady state
of VHG ethanol fermentation. Compared to initial cell status (P1), the
curve of total counts of DGEs in P2-5 fitted the biomass curve (\selectlanguage{greek}π
\selectlanguage{english}phase). The highest counts of DEGs appeared between P1 and P3 due to the
maximum time difference of P3 and P1(or P5 that is equivalent to P1).

The mRNA expression of six selected genes (upregulated during P1-P2-P3
and downregulated during P3-P4-P5) was measured by qRT-PCR. These genes
are common differential genes compared by several adjacent points. The
trend of gene expression levels in five sampling points of 6 DEGs were
consistent with that of RNA-seq, which verified the accuracy of the DEGs
from transcriptome analysis (Figure S2).

\textbf{Intermediates and gene expression associated with central carbon
metabolism}

Cells are able to assess the magnitude of metabolic flux by tuning the
rate of metabolic reactions, consequently regulating cellular physiology
under various stressful conditions (Litsios, Ortega, Wit, \& Heinemann,
2018). During CVEF with glucose as the sole carbon source and major
products such as ethanol, CO\textsubscript{2}, biomass, and glycerol,
the central carbon metabolism pathways of \emph{S. cerevisiae} cells
mainly constitute the Embden-Meyerhof-Parnas pathway (EMP), the pentose
phosphate pathway (PPP) and the tricarboxylic acid cycle (TCA) (Figure
S3). Thus, metabolomics and transcriptomic datasets of yeast were
integrated and interpreted via a pathway-based method, referring to 26
metabolites and 90 genes related to central carbon metabolism, whose
responses in an oscillation period were visualized in Figure 3.

Glucose firstly fluxes into EMP to provide ATP and precursors for cell
growth. EMP can be divided into an energy-consuming phase (--2 mol
ATP/mol glucose from GLC\textsubscript{in} to GAP) and an
energy-producing phase (4 mol ATP/mol glucose from GAP to PYR). Most of
the pyruvate produced is converted into ethanol in the condition of low
dissolved oxygen. The PPP as the source of NADPH and pentose contains an
oxidative pathway (from G6P to Ri5P) and a non-oxidative pathway (from
Ri5P to F6P and GAP). The TCA is a series of enzyme-catalyzed chemical
reactions that form a key part of aerobic respiration in cells. As to
the changing tendency of carbon metabolism during the oscillation
(Figure 3), it was weakened during the P2 to P3 phase (especially the
EMP and TCA processes) while being enhanced during the P3 to P5 with the
decreasing residual glucose and increasing ethanol concentration.

The significant decline of most metabolites with associated
down-regulated gene expression was observed in P3/P2, but the opposite
phenomenon happened in P5/P4. The reason may be found in Figure 1 where
biomass increased 3.3 g/L from P2 to P3, but decreased 3.32 g/L from P4
to P5 was observed. In addition, more changed metabolites and gene
expression appeared in P3/P2 and P5/P4 than that observed for P2/P1 and
P4/P3 and this may be due to the biomass difference between the adjacent
samples. From Figure 1, the biomass changes between P2 and P3, P4 and P5
were about two-fold of that between P1 and P2, P3 and P4. The pool of
cofactors and ATP was enhanced in P2/P1 and P5/P4, but decreased from P2
to P4, which reflected the activity of the energy metabolic reactions.
In spite of an expected sinusoidal oscillation found in Figure 3, there
are many additional details that can be elucidated by analyzing the
metabolites and genes one by one (Figure 4).

Figure 4A showed the specific concentration and changing trend of each
metabolite, most of which are involved in Figure 3, except adenylate
energy charge (EC). Throughout the oscillation cycle, ATP was the
dominant intracellular adenine nucleotide and the adenylate energy
charge (EC = ({[}ATP{]} + 0.5*{[}ADP{]})/({[}ATP{]} + {[}ADP{]} +
{[}AMP{]})) remained high (\textgreater{}0.9), which indicates that the
cell has a strong potential for phosphoryl transfer and works as the
`normally metabolizing' cell during the CVEF. In addition, we also found
that the changing tendency of EC is identical with biomass (Figure 1),
which seems to indicate that a higher EC value is benifitial for cell
proliferation. As described in Figure S1, most profiles of genes and
metabolites are able to be divided into four patterns of sin function:
0\selectlanguage{greek}π, π/\selectlanguage{english}2, \selectlanguage{greek}π, \selectlanguage{english}3\selectlanguage{greek}π/\selectlanguage{english}2 phases. As shown in Figure 4B, the changing tendencies
of 16 out of 22 genes related to EMP pathway presented a \selectlanguage{greek}π/\selectlanguage{english}2 phase,
while the rest five genes (\emph{HXK1} , \emph{HXT1,2,4} and \emph{PFK1}
) showed a 3\selectlanguage{greek}π/\selectlanguage{english}2 phase, 0\selectlanguage{greek}π \selectlanguage{english}phase and \selectlanguage{greek}π \selectlanguage{english}phase, respectively. This inferred
that the processes of glucose transport, glucose phosphorylation, and
the catalysis of PFK could be the nodes for the oscillation regulation
of intracellular metabolites. Genes \emph{PFK1} and \emph{PFK2} encoded
the 4\selectlanguage{greek}α- \selectlanguage{english}and 4\selectlanguage{greek}β-\selectlanguage{english}subunits which compose the heterooctameric
enzyme(PFK)in yeast and both types of subunits contributing equally to
catalysis instead of that one of the subunits functioning preferentially
in allosteric regulation (Heinisch, Boles, \& Timpel, 1996). It's
interesting that \emph{PFK2} was not induced more drastically between P2
and P3/4 as \emph{PFK1} was (Figure 4B), which seems to indicate
that\emph{PFK1} is upregulated more rapidly and variably than
\emph{PFK2} in response to the increased substrate concentration. In
addition, PFK activity is subject to allosteric control mechanism, with
inhibition by ATP and activation by AMP (Heinisch et al., 1996). Here,
the change trend of ATP (0\selectlanguage{greek}π \selectlanguage{english}phase) is contrary to \emph{PFK1} (\selectlanguage{greek}π \selectlanguage{english}phase),
which reflects its inhibitory effect on PFK allosteric control. But the
change trend of activator AMP is not consistent with \emph{PFKs} ,
probably because its absolute oscillation change values are much lower
than that of ATP, making its effect was not shown intuitively in the
overall trend of \emph{PFKs} . Two distinct changing tendencies for the
upper and down neighboring metabolites of PFK, G6P and F6P are in \selectlanguage{greek}π/\selectlanguage{english}2
phase while FBP and GAP are in 0\selectlanguage{greek}π \selectlanguage{english}phase. A margin of \selectlanguage{greek}π/\selectlanguage{english}2 phase between
their profiles was assumed to be induced by PFK activity to catalyze F6P
to produce FBP, indicating that PFK is the key rate-limiting enzyme in
the energy-consuming phase of the glycolytic pathway. This description
is similar to the mechanism proposed for the metabolic oscillation in
continuous culture or yeast extracts (Boiteux, Goldbeter, \& Hess, 1975;
Papagiannakis, Niebel, Wit, \& Heinemann, 2017; Thoke et al., 2018).

In ethanol production after the EMP pathway, the RPKM value
of\emph{ADH4} was always over 2000, peaking at P3 with a value of 4300,
while \emph{PDC1} and \emph{ADH1} presented a \selectlanguage{greek}π/\selectlanguage{english}2 phase, consistent with
most genes of the EMP pathway, contrary with \emph{PDC5,6}
and\emph{ADH4} (Figure 4B). The expression profiles of \emph{ADH4} and
PDC5 are \selectlanguage{greek}π/\selectlanguage{english}2 phase earlier than ethanol, and the overall expression
level of\emph{ADH4} is much higher than that of \emph{PDC5} , suggesting
that Adh4p might be responsible for ethanol formation during CVEF. The
gene\emph{ADH4} encodes the zinc-dependent alcohol dehydrogenase
isoenzyme, and its transcription is induced in response to zinc
deficiency, which was consistent with the changing tendency of the
expression level of zinc deficiency related gene \emph{ZPS1} (Figure
S2). The expressions of\emph{ADH1} and \emph{PDC1} were regulated by the
same regulatory factors, while that of \emph{ADH4} and \emph{PDC5} and
\emph{PDC6} by a different kind. It could be assumed that the regulation
of the expression of gene \emph{ADH1} and \emph{PDC1} were closely
related to the flux changes of catabolism, while the regulation of the
expression of gene \emph{ADH4} and \emph{PDC5} and \emph{PDC6} were
closely related to the flux changes of anabolism and zinc level.

In addition, yeast cells produced 10.2-13.7 g/L glycerol during CVEF,
and the changing tendency of the extracellular glycerol level showed a \selectlanguage{greek}π
\selectlanguage{english}phase pattern, which was similar to \emph{PFK1} but contrary to
the\emph{GPD1, HOR2} and the ORP value (Figure S4), while those
of\emph{GPD2} and \emph{RHR2} showed \selectlanguage{greek}π/\selectlanguage{english}2 phase pattern (Figure 4B). The
expression of \emph{GPD1} and \emph{GPD2} are regulated by osmotic
stress and redox homeostasis, and studies have shown that the HOG
pathway where the \emph{GPD1} and \emph{GPD2} are located can respond to
ethanol stress (Klein, Swinnen, Thevelein, \& Nevoigt, 2017; Udom,
Chansongkrow, Charoensawan, \& Auesukaree, 2019), suggesting that the
glycerol production during VHG oscillation was induced by osmotic stress
and the redox environment.

The expression level of genes related to PPP was far below that in the
EMP (Figure 4B), suggesting that cellular anabolism might be repressed
during CVEF. The oxidation stage of the PPP pathway is from G6P to 6PG,
and finally Ri5P. Both steps generate NADPH, which provides reducing
power and precursor metabolites for the cell's biosynthesis pathways.
The changing tendency of 6PG showed \selectlanguage{greek}π \selectlanguage{english}phase pattern, similar to biomass
(Figure 1), indicating that yeast cell growth is closely related to
NADPH synthesis. Furthermore, the expression level of \emph{ALD4, ALD6}
,\emph{MAE1} (Figure S5), and \emph{ADH4} exhibited a 3\selectlanguage{greek}π/\selectlanguage{english}2 phase
pattern, ahead of NADH and NADPH. The products of \emph{ALD4, ALD6}
and\emph{MAE1} are involved in catalyzing cofactor NAD(P)H generation
steps, suggesting that cofactor NAD(P)H generation might be the driving
force of PPP flux and biomass accumulation.

As to the TCA cycle, in this study, CVEF was conducted with aeration of
only 0.05 vvm, therefore, the metabolites fluxed into the TCA cycle
might be at low concentration, which was confirmed by transcriptional
analysis result that showed the expression levels of genes related to
TCA cycle were much lower than these related to EMP pathway.

\textbf{Dynamics of gene expression associated with stress response}

Yeast cells suffer from multiple stresses during CVEF, such as ethanol
inhibition, osmotic stress by high residual glucose, and redox stress
from imbalanced energy metabolism (Auesukaree, 2017; Burphan et al.,
2018; Caspeta, Castillo, \& Nielsen, 2015). The metabolism of storage
carbohydrates, such as trehalose, glycogen, and glycan, was investigated
to help determine tolerance to environmental stress (Babazadeh et al.,
2017; Li et al., 2009). During the oscillation, significant changes were
observed for the expression level of genes related with stress response,
such as oxidative stress response related genes \emph{SOD1, SOD2, CTT1,
OYE2} and \emph{GRX8} , and the HSP family
(\emph{HSP10,12,26,31,42,60,82} ). As shown in Figure 5, it was
difficult to draw a unified change rule for all, but most of the stress
response genes were highly expressed at P4, when ethanol levels kept
increasing in the broth and was about to achieve the maximum value. At
the same time, the glucose level approached its minimum and ORP value
was at an intermediate level, thus those two factors could not exert
severe stresses on cells, suggesting that a large proportion of cellular
response to stresses were triggered by ethanol inhibition in P4.

HSPs are a highly conserved family of molecular chaperone proteins that
play a role in the aggregation, assembly, transport, and folding of
proteins (Muhlhofer et al., 2019). Due to the common effect of multiple
stresses during CVEF on disturbing protein structures, the upregulation
of HSPs is observed upon exposures to all of these stresses, making the
expression of HSPs overall higher. \emph{HSP10} and \emph{HSP60} are
mitochondrial matrix co-chaperonin and chaperone ATPase, which
cooperates with each other, so their expression levels are
similar.\emph{HSP26} and \emph{HSP42} are small heat shock proteins that
form oligomers to suppress unfolded proteins aggregation. The expression
levels of these HSPs are consistent with changes in ethanol
concentration, indicating that cellular protein denaturation was
triggered by ethanol stress.

In addition, it seems that the ethanol oscillation was mainly
responsible for the expression changes of \emph{SOD1} . During the VHG
fermentation, \emph{S. cerevisiae} exhibited increased levels of
intracellular Reactive Oxygen Species (ROS) and oxidative damage to cell
structures. Superoxide dismutases (SODs), the major ROS-scavenging
enzymes, are classified into two groups according to their subcellular
localization and metal cofactors, namely cytosolic Cu/Zn-SOD (Sod1p) and
mitochondrial Mn-SOD (Sod2p). Although both \emph{SOD1} and
\emph{SOD2}are highly expressed in the whole oscillation for the
tolerances to ROS stress, the average and the variance of \emph{SOD1}
expression is 21.6\% and 53.7\% higher than that of \emph{SOD2} . This
may be due to the production of ethanol in the cytoplasm, which brings
greater ROS stress to this cellular compartment.

Although ethanol is a major stress trigger, we have also found additive
effects of multiple stress conditions on the expression of some stress
genes, making these genes highly expressed during the entire oscillation
cycle, especially at P3 and P4 to cope with the oxidative stress caused
by high osmotic pressure and strong ethanol inhibition. For example, the
oxidative stress response gene \emph{OYE2} (NADPH dehydrogenase) and the
glutathione-independent methylglyoxalase gene \emph{HSP31} were
initially up-regulated in response to the osmotic stress caused by high
glucose concentration (P2/P1), were subsequently further up-regulated
due to osmotic pressure and ethanol stress (P3/P2) and finally expressed
at its highest level at P4 due to strong ethanol stress.

\textbf{Dynamics of gene expression associated with anabolism}

Biomass as an important process parameter oscillated periodically in
CVEF, indicating that the anabolism of yeast cells may also experience
the changing cycle. Transcriptome analysis showed that a large amount of
genes related to ribosome synthesis were also expressed in a periodic
pattern. As shown in Figure 6, most DEGs of yeast cells at P2 were
up-regulated in comparison with P1, and also in the comparison of gene
expression of yeast cells at P5 to P4, while the DEGs of yeast cells at
P4 were down-regulated compared with P3. However, there was no
significant difference between the gene expression of yeast cells at P2
and P3. Further, the overview of changes in expression of the ribosome
synthesis genes were presented as the non-standard 0\selectlanguage{greek}π \selectlanguage{english}phase pattern that
only decreased in \selectlanguage{greek}π/\selectlanguage{english}2 period, not in the \selectlanguage{greek}π \selectlanguage{english}period. It was indicated that
ribosome synthesis was robust in the 3\selectlanguage{greek}π/\selectlanguage{english}2 period of the oscillation (the
phase from P4, P1, P2 to P3), while much lower in the remaining 1/4
oscillation period (the phase from P3 to P4).

The up-regulated expression of genes related to ribosome synthesis
symbolized the start of anabolism. Thus, it can be speculated that cell
anabolism was initiated at P4, and continued to P3 (P4-P5(P1)-P2-P3, 3/4
oscillation period), while the biomass presented as the \selectlanguage{greek}π \selectlanguage{english}phase pattern
that exist about \selectlanguage{greek}π \selectlanguage{english}phase later than the ribosome synthesis. The
increased expression of ribosomal genes prior to biomass accumulation
indicated that ribosome synthesis was a material preparation process
during the interval of cell division and that the genes related to
ribosome synthesis were prioritized and expressed. In addition, the
periodic change of ribosomal synthesis gene were opposite to the ethanol
concentration, and there was a \selectlanguage{greek}π \selectlanguage{english}phase difference between them, which
may suggest that the regulation of ribosomal synthesis during VHG
oscillation is also related to the ethanol stress.

\textbf{Discussion}

Transcriptomic and metabolomic analysis were employed in this study to
address the cellular response of yeast cells during sustained VHG
oscillation. In a typical sinusoid period, P1 was consistent with P5 due
to almost no difference in gene expression observed between them. Thus,
the VHG oscillation is regarded as a closed cycle. The end point P5 is
essentially just the start point P1 for the next period.

Among all kinds of stresses that yeast cells encounter in CVEF, dynamic
ethanol inhibition was highlighted in previous studies and recognized to
be responsible for oscillation(Wang et al., 2013). Expression level of
most genes referring to anabolism, such as ribosome synthesis pathway,
were up-regulated from phase P1 to P3, while down-regulated from phase
P3 to P5, presented as an 0\selectlanguage{greek}π \selectlanguage{english}phase pattern, suggesting that cellular
anabolism might be regulated by the similar factors. Further, combined
with the changing of ethanol, which presented as a \selectlanguage{greek}π \selectlanguage{english}phase pattern, this
suggests that cellular anabolism might be modulated by ethanol
inhibition.

A certain critical ethanol concentration (E\textsubscript{crit}) was
speculated at approximately 50 g/L (Wang et al., 2013). If the ethanol
concentration exceeded the E\textsubscript{crit}, the cell proliferation
of the yeast culture was significantly inhibited while at the same time
the biomass level was decreasing due to continuous dilution. However,
most cells still survived and produced ethanol without cell
proliferation, thus the ethanol in the broth would climb gradually to
the maximum concentration until yeast cells lost fermentation ability.
Then ethanol in the broth would decrease due to continuous dilution,
until the ethanol inhibition imposed on yeast cells was attenuated. When
the ethanol concentration declined below E\textsubscript{crit}, the
yeast cells would gradually revive the abilities of proliferation and
fermentation, such as ribosome synthesis and the biomass and ethanol
levels in the broth accumulated gradually. Unless the specific rate of
ethanol production and biomass formation passed over the dilution rate
(D=0.027 h\textsuperscript{-1}), biomass and ethanol in broth would
decrease for a long time. As soon as the yeast cells proliferated and
the ethanol production rate surpassed the dilution rate, the ethanol
concentration increased to a higher level. The ethanol inhibition
imposed on yeast cells would then become stronger and cell proliferation
and fermentation would subsequently be inhibited and another VHG
oscillation period would be initiated. Hence, the sustained VHG
oscillation is a result of the competition of cell growth, ethanol
formation and ethanol inhibition.

The concentration of metabolites in the glycolytic pathway exhibited a
cyclical oscillation behavior. There was a \selectlanguage{greek}π/\selectlanguage{english}2 phase difference between
the curve of metabolites in the energy consumption stage and the
production stage. The generation of this oscillation behavior may
originate from phosphofructokinase (PFK), indicating that the VHG
oscillation behavior may be the result of the combined effect of
intracellular regulation and extracellular ethanol stress in yeast
cells.

This work has analyzed the intracellular responses of the yeast cells to
the dynamic ethanol inhibition during VHG oscillation. The enhancement
of ethanol tolerance of the strain could alleviate the stress of ethanol
on cells, thus helping to attenuate the oscillation effect in CVEF. In
addition, further transformation of related metabolic pathways by
genetic engineering may weaken the oscillation behavior at the cellular
molecular level, which may enable VHG continuous ethanol fermentation
technology to meet industrial production requirements.

\textbf{Conclusions}

Continuous ethanol fermentation using VHG substrate by \emph{S.
cerevisiae} in a stirred tank reactor was carried out in this study, and
oscillatory behavior of fermentation parameters was exhibited
periodically and in a sustained manner with a period of approximately
130 h. Here, we also analyzed the intracellular oscillation behavior and
demonstrated that there was a lag response between macro-parameters,
extracellular metabolites, and the expression level of genes. In central
carbon metabolism, the regulation of \emph{PFK} was shown pointed to
play an important role in the regulation of the oscillations of
glycolytic metabolites. For ethanol formation, it was assumed that the
regulation of the expression of gene \emph{ADH1} and \emph{PDC1} was
closely related to the flux changes of catabolism, while the regulation
of the expression of genes \emph{ADH4} , \emph{PDC5} and \emph{PDC6} was
closely related to the flux changes of anabolism. Glycerol was formed
due to both osmotic stress response and redox homeostasis mechanisms,
but the flux of the TCA cycle was negligible during CVEF at low
oxygen-supplemented conditions. Oxidative stress response related genes
and the HSPs family genes were observed to be significantly changed and
mainly triggered by ethanol inhibition. In anabolic process, the
ribosome synthesis genes were prioritized and expressed before the
biomass change, suggesting that it needs time for cell construction.
Finally, we proposed a mechanism to describe the oscillatory process,
and the sustained VHG oscillation is considered as a result of the
competition of cell growth, ethanol formation and ethanol inhibition.

\textbf{Acknowledgements}

We appreciate financial support from National Key R\&D Program of China
from Ministry of Science and Technology of the People\selectlanguage{ngerman}´s Republic of
China {[}grant number 2018YFB1501401{]}, National Natural Science
Foundation of China {[}grant number 31500040, 21978167{]}, and the
Natural Science Foundation of Shanghai {[}grant number 18ZR1420700,
19160745300{]}. We thank Prof. Hua Zhang and Dr. Huihui Wan in State Key
Laboratory of Fine Chemicals, Dalian University of Technology for the
metabolic analysis.

\textbf{Abbreviations}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
1,3-BPG & 1,3-bis-Phospho-Glycerate\tabularnewline
\midrule
\endhead
2+3PG & 2-+3-Phosphoglycerate\tabularnewline
6PG & 6-Phosphogluconate\tabularnewline
6PGL & 6-Phospho-glucono-1,5-lactone\tabularnewline
AcCoA & Acetyl-CoA\tabularnewline
ACE & Acetate\tabularnewline
AKG & \selectlanguage{greek}α-\selectlanguage{english}Ketoglutarate\tabularnewline
ALD & Aldehyde dehydrogenase\tabularnewline
CER & Carbon dioxide evolution rate\tabularnewline
CIT CVEF & Citrate Continuous VHG ethanol fermentation\tabularnewline
DCW & Dry cell weight\tabularnewline
DEGs & Differentially expressed genes\tabularnewline
DHAP & Di-OH-acetone-phosphate\tabularnewline
E4P & Erythrose-4-phosphate\tabularnewline
EC EMP & Energy charge Embden-Meyerhof-Parnas\tabularnewline
ETH & Ethanol\tabularnewline
F6P & Fructose-6-phosphate\tabularnewline
FBP & Fructose-1,6-diphosphate\tabularnewline
FUM & Fumarate\tabularnewline
G1P & Glucose-1-phoshate\tabularnewline
G3P & 3-Phosphoglycerate\tabularnewline
G6P & Glucose-6-phoshate\tabularnewline
GAP & Glyceraldehyde-3-phosphate\tabularnewline
GLC & Glucose\tabularnewline
GOL & Glycerol\tabularnewline
GOLP & Glycerol-3-phosphate\tabularnewline
HThPP & 2-Hydroxyethyl-ThPP\tabularnewline
HSPs & Heat shock proteins\tabularnewline
ICIT & Isocitrate\tabularnewline
MAL & Malate\tabularnewline
OAA & Oxaloacetate\tabularnewline
ORP & Oxidation reduction potential\tabularnewline
OUR & Oxygen uptake rate\tabularnewline
PEP & Phosphoenolpyruvate\tabularnewline
PFK & Phosphofructokinase\tabularnewline
PPP & pentose phosphate pathway\tabularnewline
PYR & Pyruvate\tabularnewline
R5P & Ribose-5-phosphate\tabularnewline
Ri5P & Ribulose-5-phosphate\tabularnewline
RNA-Seq RPKM ROS & RNA sequencing Reads per kilobase per million mapped
reads Reactive Oxygen Species\tabularnewline
SUC & Succinate\tabularnewline
SuCoA & Succinyl CoA\tabularnewline
T6P & Trehalose-6-phosphate\tabularnewline
TRE & Trehalose\tabularnewline
VHG & Very high gravity\tabularnewline
\bottomrule
\end{longtable}

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\textbf{Figure legends}

\textbf{Figure 1} . The profiles of biomass, cell viability, living
cells (A) and residual glucose, ethanol, glycerol (B) as a sinusoidal
function with different phases (0 or \selectlanguage{greek}π\selectlanguage{english}) in an oscillation period. P1-5
represent five sampling points at the beginning (P1), bottom (P2),
middle (P3), top (P4), and end (P5) of the biomass oscillation period.

\textbf{Figure 2} . Statistical analysis for differentially expressed
genes (DEGs) according to log\textsubscript{2} ratio[?]1 or [?]-1.
Comparison between adjacent samples (left) and samples P2-5 to P1
(right).

\textbf{Figure 3} . Integrated view of metabolome and transcriptome in
central carbon metabolism pathways for neighboring samples. The gene
expression and metabolite pool were marked as squares and circles,
respectively. Red, green, and yellow represented up-regulation,
down-regulation and no significant changes between two samples. Refer to
Figure S3 for more detail of the metabolites and enzymes shown.

\textbf{Figure 4} . The concentrations of main intracellular metabolites
(A) and the gene expression level (B) as a function of time in an
oscillation period, including the main pathways of carbon metabolism,
such as EMP (Red), Ethanol production (Pink), TCA (Yellow), Glycerol
generation (Purple), Trehalose generation (Green), PPP (Blue) and other
related factors (Black).

\textbf{Figure 5.} The expression level of genes related to stress
responses.

\textbf{Figure 6.} Analysis of DEGs in the synthesis of ribosome
subunits. The DEGs between (A) P2/P1, (B) P3/P2, (C) P4/P3, (D) P5/P4,
and (E) the sin curve of DEGs total counts.\selectlanguage{english}
\begin{figure}[H]
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\includegraphics[width=0.70\columnwidth]{figures/Abstract-Art/Abstract-Art}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`200615-Figures of manuscript-by LIU.docx`  available at \url{https://authorea.com/users/333761/articles/459841-omics-analysis-reveals-the-mechanism-of-metabolic-oscillation-during-continuous-vhg-ethanol-fermentation-by-saccharomyces-cerevisiae}

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