Abstract
The thermotolerant yeast Kluyveromyces marxianus is known for its
potential in high-temperature ethanol fermentation, yet it suffers from
excess acetic acid production at elevated temperatures, which hinders
ethanol production. To better understand how the yeast responds to
acetic acid stress during high-temperature ethanol fermentation, this
study investigated its transcriptomic changes under this condition. RNA
sequencing (RNA-seq) was used to identify differentially expressed
genes (DEGs) and enriched gene ontology (GO) terms and pathways under
acetic acid stress. The results showed that 611 genes were
differentially expressed, and GO and pathway enrichment analysis
revealed that acetic acid stress promoted protein catabolism but
repressed protein synthesis during high-temperature fermentation.
Protein–protein interaction (PPI) networks were also constructed based
on the interactions between proteins coded by the DEGs. Hub genes and
key modules in the PPI networks were identified, providing insight into
the mechanisms of this yeast's response to acetic acid stress. The
findings suggest that the decrease in ethanol production is caused by
the imbalance between protein catabolism and protein synthesis.
Overall, this study provides valuable insights into the mechanisms of
K. marxianus's response to acetic acid stress and highlights the
importance of maintaining a proper balance between protein catabolism
and protein synthesis for high-temperature ethanol fermentation.
Supplementary Information
The online version contains supplementary material available at
10.1007/s44154-023-00108-y.
Keywords: Kluyveromyces marxianus, Acetic acid, Transcriptomics,
Protein–protein interaction network
Introduction
The use of biofuels has become increasingly important in recent years,
with bioethanol being one of the most widely used alternatives to
fossil fuels. Bioethanol has the potential to reduce emissions of air
pollutants (Goldemberg [42]2007; Salvo et al. [43]2017) and CO[2]
(Scully et al. [44]2021), making it a promising alternative to fossil
fuels. However, the use of sucrose- and starch-rich crops as feedstocks
for bioethanol production conflicts with food and feed production. To
overcome this issue, cellulosic ethanol has been developed as a 2nd
generation bioethanol, which utilizes lignocellulose from forestry and
agricultural residues as feedstocks.
The production of cellulosic ethanol involves pretreatment,
saccharification and fermentation. While Saccharomyces cerevisiae has
been widely used for industrial ethanol fermentation, its inability to
utilize pentose limits its application in cellulosic ethanol
production. In contrast, the thermotolerant yeast Kluyveromyces
marxianus has several advantages, including thermotolerance, high
growth rate and a broad substrate spectrum. Its ability to ferment both
hexose and pentose without genetic modification makes it a suitable
candidate for cellulosic ethanol production (Fonseca et al. [45]2008;
Nonklang et al. [46]2008). Moreover, K. marxianus can be employed for
high-temperature fermentation, which reduces cooling costs, minimizes
the risk of contamination, and allows for more efficient simultaneous
saccharification and fermentation (Limtong et al. [47]2007). Overall,
K. marxianus has great potential for use in cellulosic ethanol
production, and its unique characteristics make it a promising
alternative to S. cerevisiae in the biofuel industry.
Fermentation inhibitors such as weak acids, furan aldehydes and
phenolic compounds generated during pretreatment of lignocellulosic
feedstocks can hinder microbial growth, metabolism and ethanol
production (Wang et al. [48]2018). Among these inhibitors, acetic acid
is a major fermentation inhibitor produced during acid-catalyzed
hydrolysis of lignocellulose (An et al. [49]2015). Acetic acid has been
found to affect the growth and metabolism of K. marxianus (Martynova et
al. [50]2016; Rugthaworn et al. [51]2014), and our previous studies
have shown that K. marxianus produces more acetic acid during
high-temperature fermentation than at lower temperatures, leading to
incomplete glucose consumption and inhibited ethanol fermentation (Fu
et al. [52]2019; Li et al. [53]2021). Despite the critical role of
acetic acid in limiting high-temperature ethanol fermentation of K.
marxianus, the mechanisms underlying K. marxianus' response to acetic
acid during high-temperature fermentation have not been fully
elucidated. Therefore, studying the transcriptomic responses of K.
marxianus to acetic acid during high-temperature ethanol fermentation
is necessary to understand its response mechanism and identify
potential targets for improving ethanol production in K. marxianus.
The development of high-throughput sequencing technology has enabled
in-depth exploration of the response mechanisms of yeasts to acetic
acid stress. However, most studies have focused on S. cerevisiae,
leaving significant research gaps in the response and tolerance
mechanism of K. marxianus to acetic acid stress. Therefore, revealing
the response mechanism of K. marxianus to acetic acid stress is crucial
to improve its tolerance and promote its application in
high-temperature ethanol fermentation.
In this study, we analyzed the transcriptome changes of K. marxianus
under acetic acid stress, revealing its response mechanisms to this
stress. The findings of this study provide a scientific basis for the
construction of acetic acid-tolerant K. marxianus strains, which can
further enhance its application in ethanol fermentation.
Results
Acetic acid repressed high-temperature ethanol fermentation of K. marxianus
The cell concentration was monitored throughout the high-temperature
ethanol fermentation process. When no acetic acid was added, the
OD[600]reached a peak of ~ 9.4 within 8 h (Fig. [54]1a). When acetic
acid was added to the fermentation media, however, the growth of K.
marxianus was significantly repressed, with maximum OD[600] of ~ 4.3
and ~ 4.0 appeared at 8 h in the groups treated with 0.25% and 0.3%
acetic acid, respectively (Fig. [55]1a).
Fig. 1.
[56]Fig. 1
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Changes in (a) OD[600], concentrations of (b) glucose, (c) ethanol, (d)
glycerol, (e) xylose and (f) acetic acid during fermentation at 45°C
under different concentrations of acetic acid
According to the high-performance liquid chromatography (HPLC) results,
the metabolism of K. marxianus was also inhibited by acetic acid.
Glucose consumption in the control group was the fastest, with the
glucose consumed completely within 10 h, while the glucose in the group
treated with 0.25% acetic acid was completely consumed within more than
20 h (Fig. [58]1b). Glucose consumption in the group treated with 0.3%
acetic acid was not only the slowest, but it also stopped after 20 h,
with around 40 g/L glucose remained unconsumed (Fig. [59]1b). Acetic
acid treatment also slowed down ethanol generation. The ethanol
concentration in the control group reached a peak of 37 g/L within
12 h, while the ethanol concentrations in the groups treated with 0.25%
and 0.3% acetic acid reached corresponding maximum values of 40 and
18 g/L at around 24 h, respectively (Fig. [60]1c). Interestingly, total
glycerol production of the group treated by 0.25% acetic acid was less
than that of the control group (Fig. [61]1d), making the conversion
yield of the former (0.49 g ethanol/g sugar) higher than that of the
latter (0.45 g ethanol/g sugar) (Table [62]1). To our surprise,
although treatment with 0.25% acetic acid inhibited cell growth and
slowed down ethanol generation, the specific productivity in this group
was significantly higher than the that in the control group (Table
[63]1). The growth rates and metabolite production rates are in the
Fig. S[64]1, the consumption rate of xylose was almost zero, and within
6 h, the yeast's growth rate and metabolite production rate reached
their peaks.
Table 1.
Fermentation results (0–24 h) in this study
Fermentation parameters 0% HAc 0.25% HAc 0.3% HAc
Initial OD[600] 3.19 3.19 3.19
Final OD[600] 9.74 4.19 3.76
Volumetric productivity (g/L h^−1) 1.31 1.43 0.61
Specific productivity (g/OD[600] h^−1) 0.13 0.34 0.16
Conversion yield (g ethanol/g sugar) 0.45 0.49 0.44
[65]Open in a new tab
Descriptive statistics of RNA-seq data
During the early stage of acetic acid stress in yeast cells,
genome-wide alterations in transcription occur (Geng et al. [66]2017).
In order to reveal the transcriptomic responses of K. marxianus induced
by acetic acid, the cells in the group treated with 0.25% acetic acid
and the control group were sampled at 2 h, and then subjected to total
RNA extraction. Both 0.25% and 0.3% acetic acid could significantly
inhibit cell growth (Fig. [67]1a), but the concentrations of produced
ethanol were almost the same between 0.25% acetic acid treated group
and the control group (Fig. [68]1c). Therefore, to rule out the
differences in gene expression caused by different concentrations of
ethanol, the concentration of 0.25% acetic acid was chosen for RNA-seq.
Based on RNA quality evaluation (Table S[69]1), the RNA samples were
qualified for library construction. Paired-end sequencing generated
297.7 million raw reads, ranging from 42.3 to 56.9 million per sample.
After filtering, 41.7–56.3 million clean reads were obtained per sample
(Table S[70]2). HISAT2 alignment showed 92.97–95.07% of clean reads
uniquely mapped to the reference genome (Table S[71]3). We also
performed principal component analysis (PCA) to investigate if samples
with the same treatment cluster together. According to the PCA result,
the first two principal components explained more than 89% of the
variability among the samples, and acetic acid treated samples and
control samples were grouped in different clusters (Fig. S[72]2). This
result indicated that the transcriptome profiles was significantly
changed after acetic acid treatment. The acetic acid treated samples
fell in the negative direction of the PC1 axis, while the control
samples fell in the positive direction. In PC2, one sample in the
control group did not cluster with others.
Identification of DEGs and functional enrichment analysis
According to differential expression analysis, 611 DEGs (fold
change > 2 or < 0.5, P-adjust < 0.05) were identified in the samples
treated with 0.25% acetic acid compared with the control group, with
166 up-regulated and 445 down-regulated (Fig. [73]2a). Among the
up-regulated DEGs, those with fold changes > 10 accounted for 18.07%,
those with fold changes between 5 and 10 accounted for 35.54%, and
those with fold changes between 2 and 5 accounted for 46.39%. Among the
down-regulated DEGs, those with fold changes < 0.1 accounted for 4.49%,
those with fold changes between 0.1 and 0.2 accounted for 8.31%, and
those with fold changes between 0.2 and 0.5 accounted for 87.19%. The
DEGs were functionally categorized into GO functional classes and KEGG
pathways (Fig. S[74]3).
Fig. 2.
Fig. 2
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Volcano plot of the differentially expressed genes (DEGs) induced by
acetic acid
To further analyze the functions of the DEGs induced by acetic acid, GO
and KEGG enrichment analyses were conducted. According to the result of
GO enrichment analysis (Fig. [76]3a and Table S[77]4), the
significantly enriched GO terms in the DEGs were related to energy
metabolism, such as pyruvate metabolic process (GO:0006090), glycolytic
process (GO:0006096), glucose metabolic process (GO:0006006), and ATP
generation from ADP (GO:0006757), which are closely linked to energy
metabolism. GO terms related to protein synthesis, such as small
ribosomal subunit (GO:0015935), large ribosomal subunit
(GO:0015934) and cytosolic small ribosomal subunit (GO:0022627),
suggest the involvement of ribosome in the cellular response to acetic
acid. Furthermore, the result of KEGG pathway enrichment show that
ribosome (map03010), fructose and mannose metabolism (map00051),
glycolysis/gluconeogenesis (map00010), TCA cycle (map00020), proteasome
(map03050), etc. were enriched in the DEGs (Fig. [78]3b and Table
S[79]5).
Fig. 3.
Fig. 3
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Enrichment analysis of the DEGs in this study. a GO terms enriched in
the DEGs; b KEGG pathways enriched in the DEGs. Rich factor is the
ratio of DEG number annotated in this GO term (or KEGG pathway) to all
gene number annotated in this GO term (or KEGG pathway). Greater rich
factor means greater effect of acetic acid on the analyzed GO term (or
KEGG pathway). NP: nucleotide phosphorylation; NDP: nucleoside
diphosphate phosphorylation; PMP: pyruvate metabolic process; RDMP:
ribonucleoside diphosphate metabolic process; AMP: ADP metabolic
process; CLRS: cytosolic large ribosomal subunit; PNDMP: purine
nucleoside diphosphate metabolic process; PRDMP: purine ribonucleoside
diphosphate metabolic process; GP: glycolytic process; AGA: ATP
generation from ADP; LRS: large ribosomal subunit; SRS: small ribosomal
subunit; CSRS: cytosolic small ribosomal subunit; CT: cytoplasmic
translation; SCR: structural constituent of ribosome; NDMP: nucleoside
diphosphate metabolic process; RB: ribosome; RS: ribosomal subunit;
GMP: glucose metabolic process; ABP: amide biosynthetic process; FMM:
fructose and mannose metabolism; G/G: Glycolysis/Gluconeogenesis; PT:
proteasome; ASNSM: amino sugar and nucleotide sugar metabolism; GM:
galactose metabolism; NKGB: neomycin, kanamycin and gentamicin
biosynthesis; MM: methane metabolism; TCA: citrate cycle (TCA cycle);
SSM: starch and sucrose metabolism; PR: peroxisome; PCM: porphyrin and
chlorophyll metabolism
Therefore, based on the enrichment results, it can be inferred that
acetic acid may act on mitochondria, ribosome-related molecular
functions, cellular components, biological processes, etc. to influence
the energy conversion process in cells and thus the growth and
metabolism of microorganisms.
PPI network analysis
In order to further elucidate the response mechanism to acetic acid
stress, PPI networks of all DEGs, up-regulated DEGs and down-regulated
DEGs were constructed, respectively. Four hundred and seventeen
proteins associated with the DEGs were matched with the STRING database
and used to construct the PPI networks. The threshold of interaction
score was set to > 0.4 and the unconnected nodes were hidden. The
constructed PPI networks of all DEGs consisted of 329 nodes and 2041
edges (Fig. [81]4), while those of up-regulated DEGs and down-regulated
DEGs consisted of 73 nodes, 116 edges (Fig. S[82]4), and 225 nodes,
1612 edges (Fig. S[83]5), respectively. In the PPI network of the
proteins coded by all the DEGs, there were many tightly interconnected
nodes corresponding to down-regulated genes associated with ribosome
(such as RPL3, PRL4B, RPL25, RPS6, RPL40B and RPS1), while the nodes
corresponding to up-regulated genes were relatively loosely connected
(Fig. [84]4). Based on the PPI network of up-regulated DEGs, the nodes
corresponding to proteasome-related genes (such as RPN5, RPN6 and
RPN11) exhibited tight interconnections (Fig. S[85]4), indicating that
these genes were coordinately involved in the cellular response to
acetic acid stress.
Fig. 4.
[86]Fig. 4
[87]Open in a new tab
Protein–protein interaction (PPI) network of the proteins coded by the
DEGs in this study. Red nodes represent proteins coded by up-regulated
DEGs; green nodes represent proteins coded by down-regulated DEGs;
edges represent protein–protein interactions, and thicker edges
indicate stronger interactions
To obtain the major PPI network of up-regulated DEGs, the topological
connectivity of each node was determined based on the centrality
parameters degree, betweenness and eigenvector. According to the
analyses of centrality parameters, twenty-two, eighteen and sixteen
nodes were identified from the PPI network of up-regulated DEGs with
the values of degree, betweenness and eigenvector above average,
respectively. Seven nodes showed all these three centrality parameters
above average and formed the major PPI network of up-regulated DEGs
with 12 edges, which was equivalent to 24.1% of the PPI network of
up-regulated DEGs (Fig. [88]5a and Fig. S[89]6). These hub genes were
ranked by MCC value (Fig. [90]5b and Table S[91]6). A single module was
identified from this major PPI network (Fig. [92]5c). Similarly, the
major PPI network of down-regulated DEGs with 32 nodes and 316 edges
was obtained (Fig. [93]6a and Fig. S[94]7). The top 10 hub genes with
higher MCC values were identified and sequentially ordered (Fig. [95]6b
and Table S[96]7). In addition, three significant modules were
identified via the MCODE plugin (Fig. [97]6c).
Fig. 5.
Fig. 5
[98]Open in a new tab
Major PPI networks of up-regulated DEGs and module analysis. a Venn
diagram showing nodes with values of centrality parameters (degree,
betweenness, and eigenvector) above average. b Major PPI network of
up-regulated hub genes and MCC ranking of these genes. Node color
reflects the degree of connectivity, the redder a node, the greater its
MCC value. c MCODE analysis. Module 1: score = 4.5
Fig. 6.
[99]Fig. 6
[100]Open in a new tab
Major PPI networks of down-regulated DEGs and module analysis. a Venn
diagram showing nodes with values of centrality parameters (degree,
betweenness, and eigenvector) above average. b Major PPI network of top
10 down-regulated hub genes with highest MCC values and MCC ranking of
these genes. Node color reflects the degree of connectivity, the redder
a node, the greater its MCC value. c MCODE analysis. Module 1:
score = 20.8; Module 2: score = 4; Module 3: score = 3. Triangles
represent hub genes; the sizes of the circles indicate the MCC values
To further investigate the functions of genes in the identified
modules, we performed GO and KEGG enrichment analyses for these genes.
We found that GO terms and KEGG pathways related to proteasome were
enriched in the only identified module of major PPI network of
up-regulated DEGs (Tables S8-S9). For the major PPI network of
down-regulated DEGs, GO terms associated with ribosome, mitochondrial
ribosome and mitochondrial translation were enriched in modules 1, 2
and 3, respectively, and a KEGG pathway related to ribosome was also
enriched in module 1, but no KEGG pathway was enriched in modules 2 and
3 due to the limited numbers of nodes in these modules. These results
indicated that acetic acid stress promoted protein catabolism but
repressed protein synthesis, which affected the growth and metabolism
of K. marxianus and led to the decrease of ethanol production. In order
to further verify this inference, we extracted and quantitatively
determined total protein content in the yeast cells expose to 0.25%
acetic acid (treatment group) and the control group. Compared with the
control group, the protein content in K. marxianus decreased by 55.41%
under acetic acid stress (Fig. S[101]8), which confirmed the
bioinformatic results.
Discussion
K. marxianus, a thermotolerant yeast capable of thriving at 45℃,
exhibits potential for industrial ethanol production, in contrast to
other yeasts, such as K. lactis and S. cerevisiae (Kosaka et al.
[102]2022). Despite its advantageous features, acetic acid, a byproduct
of acid-catalyzed hydrolysis of lignocellulose, constitutes one of the
main fermentation inhibitors affecting growth, metabolism and ethanol
production in K. marxianus, especially under high-temperature
fermentation conditions. Undissociated acetic acid permeates through
the plasma membrane and splits into H^+ and CH[3]COO^−, thereby
acidifying the cell cytoplasm and impeding cellular metabolic
activities, eventually leading to cell death (Arneborg et al.
[103]2000; Casal et al. [104]1996). Although previous studies in S.
cerevisiae exposed to acetic acid have documented significant changes
in gene expression at the transcriptional level (Geng et al.
[105]2017), little is known about the transcroptomic changes and the
involved molecular mechanisms that K. marxianus utilizes to counteract
acetic acid stress.
In the current study, we conducted high-temperature ethanol
fermentation and performed PCA analysis to evaluate the effects of
acetic acid treatment on the samples. The results indicate that the PC1
axis represents the primary source of variation between the acetic
acid-treated samples and the control samples. The acetic acid treatment
potentially exerts a significant influence on the transcriptome profile
of the samples. We then analyzed the transcriptome of K. marxianus
cells exposed to 0.25% acetic acid and identified 611 DEGs, with 166
up-regulated and 445 down-regulated DEGs. GO and KEGG enrichment
analyses were performed on DEGs to understand their biological
functions. GO terms related to ribosome were significantly enriched
under acetic acid stress (such as GO:0022625, GO:0015934, GO:0015935,
GO:0022627, GO:0005840, GO:0044391, GO:0002181 and GO:0003735)
(Fig. [106]3a and Table S[107]4). Meanwhile, KEGG pathway ribosome
(map03010) was significantly enriched (Fig. [108]3b and Table S[109]5).
Ribosomes are highly involved in the synthesis of proteins, and thus it
can be hypothesized that they may be a primary target of acetic acid
treatment. A study conducted on S. cerevisiae reported that exposing
the cells to acetic acid (150 mM, pH 3.0) resulted in reduced
expression of numerous ribosomal 40S and 60S subunits, leading to a
considerable decline in protein synthesis (Dong et al. [110]2017).
Furthermore, the GO terms and KEGG pathways enriched were primarily
related to energy metabolism (including GO:0006090, GO:0046031,
GO:0006096, GO:0006757, map00010 and map00020). Since mitochondria are
an important center for energy production, metabolism, signaling and
cell cycle (McBride et al. [111]2006), they may potentially be another
target for acetic acid treatment. This conclusion is supported by a
study showing that exposure of S. cerevisiae cells to acetic acid
(300 mM) causes down-regulation of genes encoding mitochondrial
ribosomal proteins at the transcriptional level, indicating that
mitochondria play a vital role in the cellular response to acetic acid
(Li and Yuan [112]2010). Additionally, proteasome (map03050) and
peroxisome (map04146) could potentially be further targets in response
to acetic acid treatment (Fig. [113]3b and Table S[114]5). These
findings indicate that acetic acid may impact various aspects of
cellular metabolism.
We further constructed PPI networks of all identified DEGs (Fig. [115]4
and Figs. S[116]4-S[117]5) and identified hub nodes in major PPI
networks of up-regulated (Fig. S[118]6) and down-regulated (Fig.
S[119]7) DEGs. This enabled us to infer potential regulatory genes and
dominant pathways in response to acetic acid. The hub nodes of
down-regulated DEGs are mainly relevant to cellular components,
biological processes and molecular functions of ribosomes (Table
S[120]7), and all 10 hub nodes are in functional module 1 of the major
PPI network of down-regulated DEGs (Fig. [121]6c), which are associated
with ribosome-related GO terms and KEGG pathways (Table S[122]8 and
Table S[123]9). A similar trend was observed in the transcriptomic
analysis of formic acid stress response in S. cerevisiae, with the
expression levels of genes involved in ribosome synthesis being
down-regulated (Zeng et al. [124]2022). Ribosomal proteins play a
crucial role in cell growth and proliferation (Petibon et al.
[125]2021). Under acetic acid stress, the down-regulation of genes such
as RPL3, PRL4B, RPL25, RPS6, RPL40B and RPS1 may lead to incomplete
ribosome structure, inhibiting the translation process, and lowering
the levels of essential proteins. This ultimately affects the growth
and metabolic activity of K. marxianus and reduces its ethanol
production. The GO terms of module 2 and module 3 are related to
mitochondrial ribosomes and mitochondrial translation (Table S[126]8).
Mitochondria, as an essential eukaryotic organelle, has its own genome
that encodes proteins necessary for normal mitochondrial function. Some
mitochondrial proteins can regulate mitochondrial acetate levels and
play a significant role in acetate detoxification, which is critical
for mitochondrial function (Fleck and Brock [127]2009; Orlandi et al.
[128]2012). Mitochondrial ribosomes are responsible for protein
synthesis inside mitochondria. The genes MRPL31, RSM28 and MNP1 encode
54S ribosomal protein L31, 37S ribosomal protein RSM28 and 39S
ribosomal protein L12, respectively. These genes participate in the
biogenesis of mitochondrial ribosomes and play an active role in
mitochondrial translation. The gene MHR1 is involved in repairing
mitochondrial DNA double-strand breaks and encodes mitochondrial
homologous recombination protein 1, which is crucial for maintaining
mitochondrial function and repairing mitochondrial DNA lesions (Prasai
et al. [129]2018). PET122 is necessary for the translation of
cytochrome c oxidase subunit III (Ohmen et al. [130]1988), a component
of the electron transport chain that is essential for ATP production.
Under acetic acid stress, these genes associated with mitochondrial
ribosomes and translation are suppressed, which may lead to decreased
ATP production and disrupted cellular functions, such as reduced
mitochondrial respiration and impaired cell growth.
The hub nodes of up-regulated DEGs are mainly related to the cellular
component and biological processes of proteasomes (Table S[131]6). The
26S proteasome is a large multi-subunit complex responsible for protein
degradation in eukaryotic cells (Bard et al. [132]2018). Excluding HAT1
and DOA4, the other five hub genes constitute functional module 1 in
the major PPI network of up-regulated DEGs (Fig. [133]5c). The GO and
KEGG enrichment analysis results suggest that the GO terms and KEGG
pathways related to proteasomes were enriched in module 1 (Tables
S[134]8-S[135]9). DOA4 encodes the deubiquitinating enzyme Doa4p, which
is central to the yeast ubiquitin-dependent proteolytic system (Papa
and Hochstrasser [136]1993). This enzyme is associated with the yeast
26S proteasome and removes ubiquitin from protein hydrolysis
intermediates on the proteasome before or after substrate degradation
to promote protein hydrolysis (Papa et al. [137]1999). Under acetic
acid stress, up-regulated genes such as RPN6, RPN11 and RPN5, which are
associated with the proteasome, may accelerate the assembly of
proteasomes and speed up the degradation of intracellular proteins. The
up-regulation of DOA4 may facilitate the ubiquitin-dependent protein
catabolic process and assists the proteasome in further acceleration of
protein degradation. Meanwhile, HAT1 encodes the catalytic subunit of
the HAT-B complex, specifically modifying Lys12 of free histone H4, and
it plays crucial roles in chromatin structure, transcription
activation, DNA repair, gene silencing and cell-cycle progression
through histone acetylation (Carrozza et al. [138]2003; Kurdistani and
Grunstein [139]2003; Rosaleny et al. [140]2005). The up-regulation of
HAT1 may increase histone acetyltransferase 1 activity, leading to a
higher level of histone acetylation. This, in turn, can cause changes
in gene expression, activate replication origins earlier, and
accelerate the correct repair of DNA damage in yeast to cope with the
adverse external environment.
Materials and methods
Strain, media and culture conditions
K. marxianus DMKU3-1042 (Limtong et al. [141]2007), which was purchased
from NITE Biological Resource Center with the deposit number of NBRC
104275, was used throughout this study. YPD medium (10 g/L yeast
extract, 20 g/L peptone and 20 g/L glucose) was used for pre-culture of
the yeast. After overnight pre-culture in flasks with shaking at 45°C,
yeast cells were washed with sterilized water and inoculated into
100-mL serum bottles with 30 mL fermentation medium (10 g/L yeast
extract, 20 g/L peptone, 80 g/L glucose and 40 g/L xylose) in each
bottle. The initial optical density at 600 nm (OD[600]) of yeast cells
in each bottle was set as ~ 3.0. To investigate the effect of acetic
acid on ethanol fermentation, the concentrations of acetic acid in the
fermentation media were set as 0%, 0.25% and 0.3% (w/v), respectively.
All fermentation experiments were conducted at 45°C with three
biological replicates.
Quantitative analyses of substrates and extracellular metabolites
Broth samples were collected at intervals throughout the fermentation
process. The samples were centrifuged at 10,000 × g for 1 min. Then the
supernatants were diluted by 0.05 mol/L H[2]SO[4] for 20 times and
filtered through filters with 0.45-µm pores. The cell pellets were
flash-frozen in liquid nitrogen and stored at -80℃ for subsequent
analysis. The concentrations of glucose, xylose, acetic acid, ethanol
and glycerol in the fermentation broth were measured by a HPLC system
equipped with an RID-20A refractive index detector (Shimadzu, Japan)
and an Aminex HPX-87H column (Bio-Rad, Hercules, CA, USA). The mobile
phase was 0.05 mol/L H[2]SO[4] with a flow rate of 0.6 mL/min. The
column temperature and detector temperature were both set as 40°C.
The fermentation parameters were calculated as follows:
[MATH: volumetricproductivity(g/Lh-1)=ethanolproduced(g/L)time(h) :MATH]
1
[MATH: specificproductivity(g/OD600h-1)=volumetricproductivity(g/Lh-1)finalOD600
:MATH]
2
[MATH: conversionyield(gethanol/gsugar)=ethanolproduced(g/L)sugarconsumed(g/L) :MATH]
3
High-throughput RNA sequencing (RNA-seq) and bioinformatic analysis
To investigate the transcriptomic responses of the yeast to acetic
acid, yeast cells collected at the 2nd hour of high-temperature
fermentation under the condition of 0.25% acetic acid (treatment group)
and no acetic acid (control group) were subjected to high-throughput
RNA-seq. Three biological replicates were carried on for RNA-seq
experiments. Total RNA samples were extracted from the cell pellets
using the EZNA Yeast RNA Kit (Omega Bio-tek, Doraville, CA, USA) and
then sent to Shanghai Majorbio Bio-pharm Technology Co., Ltd.
(Shanghai, China) for quality and quantity evaluation, cDNA library
construction and high-throughput sequencing. The genome sequence of K.
marxianus DMKU3-1042 in the NCBI database (accession number:
PRJDA65233) (Lertwattanasakul et al. [142]2015) was used as the
reference genome. After removing the adaptors and the low-quality
reads, the clean reads were aligned to the reference genome using
HISAT2 (Kim et al. [143]2015). The differentially expressed genes
(DEGs) were identified using DESeq2 (Love et al. [144]2014). The
resulting P values were adjusted using the Benjamin and Hochberg’s
approach for controlling the false discovery rate. Genes with adjusted
P (P-adjust) values less than 0.05 found by DESeq2 were considered as
differentially expressed. Gene Ontology (GO) enrichment analysis of the
DEGs was performed using the GOseq R package (Young et al. [145]2010).
KOBAS software was used for Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathway enrichment analysis (Mao et al. [146]2005). GO terms and
KEGG pathways with P-adjust values less than 0.05 were considered
significantly enriched.
Protein–protein interaction (PPI) network analysis
The STRING database ([147]http://string-db.org) (Szklarczyk et al.
[148]2021) was used to construct PPI networks of the identified DEGs.
Given that the PPI information of K. marxianus was not included in the
STRING database, we chose Kluyveromyces lactis as the reference. Then
the PPI network data was imported into the Cytoscape software for
subsequent analysis (Shannon et al. [149]2003). The centrality
parameters (degree, betweenness, eigenvector) were analyzed using
CentiScaPe 2.2 (Scardoni et al. [150]2009). Nodes with higher
centrality values than average were identified as hub nodes. The major
PPI networks were constructed based on the intersection of the hub
nodes identified based on the three selected centrality parameters. The
most significant modules in a major PPI network were identified using
Molecular Complex Detection (MCODE) plugin (Bader and Hogue [151]2003)
with a K-score value of 5. The hub genes in a PPI network were ranked
based on the MCC algorithm in CytoHubba plugin of Cytoscape (Chin et
al. [152]2014).
Quantitative protein assay
To quantify the protein content in yeast cells, we first collected
yeast cells at the 2nd hour of high-temperature fermentation under the
condition of 0.25% acetic acid (treatment group) and no acetic acid
(control group), respectively. Then total protein was extracted using
the Yeast Total Protein Extraction Kit (Sangon Biotech, Shanghai,
China) and quantitative protein assay was performed using Bradford
Protein Assay Kit (Sangon Biotech, Shanghai, China) following the
manufacturer's instructions.
Supplementary Information
[153]Additional file 1.^ (1.6MB, docx)
Abbreviations
RNA-seq
RNA sequencing
DEG
Differentially expressed gene
GO
Gene ontology
KEGG
Kyoto Encyclopedia of Genes and Genomes
PPI
Protein-protein interaction
Authors’ contributions
PL conceived and designed research. YL, SH, ZR, SF, SW, MC and HL
conducted experiments. YL, SH, ZR and YD analyzed data. YL, SH, ZR, SF,
SW, MC, YD, HL, SL and PL wrote the manuscript. All authors read and
approved the manuscript.
Funding
This work was supported by the National Undergraduate Training Program
for Innovation and Entrepreneurship (202110022074, 202198039), and
Beijing Municipal Education Commission through the Innovative
Transdisciplinary Program "Ecological Restoration Engineering".
Availability of data and materials
The raw data were deposited to the China National GeneBank database
(CNGBdb) under the accession number of CNP0004221.
Declarations
Ethics approval and consent to participate
This manuscript does not contain any studies with human participants or
animals performed by any of the authors.
Consent for publication
All authors consented on the publication of this work.
Competing interests
The authors declare that they have no competing interests.
Footnotes
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References