Abstract
Oxygen limitation is regarded as a useful strategy to improve enzyme
production by mycelial fungus like Aspergillus niger. However, the
intracellular metabolic response of A. niger to oxygen limitation is
still obscure. To address this, the metabolism of A. niger was studied
using multi-omics integrated analysis based on the latest GEMs
(genome-scale metabolic model), including metabolomics, fluxomics and
transcriptomics. Upon sharp reduction of the oxygen supply, A. niger
metabolism shifted to higher redox level status, as well as lower
energy supply, down-regulation of genes for fatty acid synthesis and a
rapid decrease of the specific growth rate. The gene expression of the
glyoxylate bypass was activated, which was consistent with flux
analysis using the A. niger GEMs iHL1210. The increasing flux of the
glyoxylate bypass was assumed to reduce the NADH formation from TCA
cycle and benefit maintenance of the cellular redox balance under
hypoxic conditions. In addition, the relative fluxes of the EMP pathway
were increased, which possibly relieved the energy demand for cell
metabolism. The above multi-omics integrative analysis provided new
insights on metabolic regulatory mechanisms of A. niger associated with
enzyme production under oxygen-limited condition, which will benefit
systematic design and optimization of the A. niger microbial cell
factory.
Introduction
With a GRAS (generally regarded as safe) status, Aspergillus niger is
widely applied in the biosynthesis of organic acids and enzymes^[44]1.
A. niger has an excellent ability of protein expression and secretion,
such as for the industrial production of glucoamylase^[45]2. In 2007,
the genome sequence and annotation information of A. niger was
published^[46]3, which became an important basis for systems biology
studies of A. niger. Genome annotation of A. niger showed its huge
potential as an efficient cell factory^[47]3 for the production of
different enzymes and secondary metabolites. A genome-scale metabolic
model (GEMs) was also reconstructed based on the genome annotation of
A. niger^[48]4. Recently, the GEMs of A. niger was further updated in
our lab^[49]5. Centering on enzyme and protein production, separate
studies on A. niger metabolomics, transcriptomics and proteomics have
been conducted^[50]6.
In the industrial enzyme production by A. niger, the poor solubility of
oxygen limits mass transfer in bioreactors. This is further aggravated
by the complex mycelial morphology, resulting in oxygen limitation for
the cell metabolism. As the growth of A. niger is strictly
aerobic^[51]7, a limited oxygen supply has a strong impact on the
fermentation process. For citric acid production, the limited oxygen
supply leads to a significant increase in productivity^[52]8. Although
the specific production rate of glucoamylase was decreased due to the
oxygen limitation, its yield per unit of substrate was increased^[53]2.
The similar results could be found in exogenous protein production by
Pichia pastoris^[54]9 and other microorganisms. It has been found that
large amounts of organic acids (like oxalic acid and citric acid) and
polyols (like mannitol and erythritol) were secreted by A. niger under
oxygen limited conditions^[55]10,[56]11, indicating a high
intracellular redox level. These microbial physiology phenomena
indicate that the metabolic balance between cell growth and product
synthesis is sensitive to oxygen limitation in A. niger. However,
details of the global metabolic changes and their interpretation in
terms of regulatory mechanisms has only been subject of little
systematic research.
Omics studies play an increasingly important role in investigation of
the cell metabolic response and regulation mechanisms, and there are a
few studies using these approaches to study how cells adapt to oxygen
limited conditions. Via transcriptome analysis of Trichoderma reesei,
it was found that the expression of genes from metabolic pathways
related to the energy consumption were significantly down-regulated in
response to the limited oxygen supply^[57]12. Using proteomics
analysis, it was shown that the expression of 117 proteins in A.
fumigatus, involved in the PP pathway, TCA pathway and EMP pathway, was
up-regulated to adapt to the hypoxic environment^[58]13. With the aid
of transcriptome analysis, Choi et al.^[59]14 found that the genes
involved in the sterol regulatory synthesis pathways were activated
under oxygen limited conditions, which facilitated the synthesis of
sterols and maintained the cell mycelial growth capabilities.
Compared with single omics analysis, a multi-omics integrative analysis
could help to reveal interactions among different metabolic regulation
levels. Based on the evidence from transcriptome analysis and molecular
experiments, Kroll et al.^[60]15 found that the electron transport
chain plays an important role in sensing the extracellular oxygen
concentration and transmitting the hypoxia signal to the mitochondria.
The metabolic characteristics of glucoamylase production by integration
of ^13C metabolic flux analysis and metabolomics^[61]16 was recently
carried out in our lab. The results showed that the intracellular
metabolic fluxes were redistributed to respond the enzyme synthesis and
redox balance. Baumann et al.^[62]17 studied the metabolic mechanism of
Pichia pastoris under oxygen limited conditions with integrative
analysis of metabolomics, transcriptomics and proteomics, and found
that flux changes in the PP, TCA and EMP pathways were mainly regulated
at a transcriptional level.
To better understand the mechanisms supporting a high yield of
glucoamylase production and global metabolic regulation under oxygen
limitation, the multi-omics integrative analysis based on GEMs is
employed, which provides holistic views for the rational optimization
of industrial bioprocess and strain performance.
Materials and Methods
Strains and cultivations
The glucoamylase high-producing strain Aspergillus niger DS03043,
donated by DSM (Netherlands) was used in all the cultivations in this
work. To obtain spores, Petri dishes containing PDA (Potato Dextrose
Agar) medium were incubated with spores from a frozen stock (stored in
50% glycerin at −80 °C). During seed culture, 500 mL shake flasks with
baffles were inoculated with 10^7 spores per 100 mL broth. A 5 L
fermentor with an electronic balance was used for the fed-batch
cultivations with the agitation rate at 375 rpm and the aeration at 1
vvm. During the cultivation, the overpressure was maintained at
0.05 MPa and the temperature was at 34 °C. The broth pH was maintained
at 4.5 by addition of NH[3] solution (5% w/w). The working volume for
the 5 L fermenter during batch cultivation was 3 L. When the glucose
concentration reduced to 5 g/L (after about 36 h of the fermentation)
during the batch cultivation, the feed was started and the glucose
concentration was kept at around 5 g/L by adjusting the feed rate.
Concentrations of oxygen and carbon dioxide in the exhaust gas were
determined by process mass spectrometers (MAX300-LG, Extrel) during the
fermentation and the dissolved oxygen concentration in the broth was
determined with a low-drift polarographic electrode (Mettler Toledo).
The medium for the seed and fed-batch fermentations can be found in the
literature reported by Lu^[63]18.
Quantification of biomass and enzyme activity
10 mL fermentation broth was filtered by filter paper, pre-weighed and
pre-dried to a constant weight (at 80 °C for 24 h). Biomass was rinsed
three times with deionized water and dried at 80 °C for 24 h. Dried
biomass was re-weighed immediately. Enzyme activity of all samples was
determined by a standard procedure^[64]18.
Quantification of extracellular sugar and organic acids
Residual sugar from the fermentation broth was determined by a glucose
analyser (Shandong Academy of Sciences, China). Extracellular organic
acids (acetic acid, citric acid, oxalic acid, malic acid, fumaric acid,
pyruvic acid and succinic acid) were determined by high performance
liquid chromatography (HPLC). The HPLC was equipped with an ion
exclusion column and an absorption detector spectrophotometer. 10 mM
H[2]SO[4] was used to wash the ion exclusion chromatography column with
the flow rate of 0.5 mL/min at 50 °C and the wavelength of the
spectrophotometer was set at 210 nm.
Sampling and quantitative analysis of intracellular metabolites
The protocol for quantitative analysis of intracellular metabolites was
modified based on Douma et al.^[65]19. Using fast sampling equipment,
1–2 ml broth was pumped from the 5 L fermenter into a 10 ml precooled
quenching solution (40% v/v methanol solution at −27.6 °C) at 18 h,
24 h, 36 h, 48 h, 60 h, 72 h and 96 h, respectively. The tubes were
weighed before and after the sampling procedure to estimate the exact
amount of broth. Then, extracellular metabolites were removed by vacuum
filtration and filter cake was washed by 120 ml precooled quenching
solution. Isotope dilution mass spectrometry (IDMS)^[66]20 was used in
this work for the quantification of metabolite concentrations. Washed
filter cake, as well as ^13C internal standard solution was added to
25 ml pre-warmed 75% (v/v) ethanol solution and the extraction
continued for 3 minutes at 95 °C. The metabolites concentration was
determined with UPLC-MS/MS (Thermo Fisher Scientific Corporation) and
GC-MS.
As for metabolomics data, the principal component analysis (PCA) and
partial least square discriminant analysis (PLS-DA) were conducted
based on the R programming language. If the variable importance of the
projection (VIP) score of one metabolite is above 1, it means that the
pool size of this metabolite changes significantly in different
fermentation phases. The Heatmap analysis of the metabolomics data from
different fermentation phases was conducted using the superheat package
of the R language
([67]https://cran.r-project.org/web/packages/superheat/).
Transcriptome analysis
According to the online DO profile, the sampling time for RNA-seq
analysis was set at 18 h, 24 h, 42 h and 66 h, which corresponding to
the logarithmic phase and early, middle, late phases of oxygen
limitation, respectively. After sampling, the broth was immediately
frozen in liquid nitrogen and stored at −80 °C. These frozen samples
were sent to Sangon Biotech for RNA extraction and RNA samples were
sent to the Beijing Genomics Institute (BGI) for sequencing. Qualified
RNA samples of each sampling time were ensured with at least 2
replicate samples for sequencing. Gene expression data of different
phases was clustered by the Mfuzz package based on R language^[68]21.
The DAVID database was used for GO enrichment analysis of the
interested gene sets^[69]22. The Piano package based on the R language
was used for KEGG pathway and GO function enrichment analysis of
differentially expressed genes^[70]23. For gene set analysis, a
mapping, established between genes of A. niger and the KEGG pathway and
the GO function, as well as gene expression data of different phases,
were used as inputs of the Piano package for statistical analysis. For
more details, please refer to the literature^[71]23.
Flux simulation using GEMs
Flux balance analysis (FBA) based on constraints is widely exploited in
the fields of genome-scale metabolic network reconstruction and
cellular phenotypic prediction^[72]24. FBA is used for prediction and
analysis of intracellular fluxes with the optimization of an objective
function under constraints. Constraints^[73]25 could be an
intracellular metabolite balance (Equation [74]2), reaction
reversibility, a maximum enzyme reaction rate and an exchange reaction
rate (Equation [75]3). An objective function is for example
maximization of cell growth or optimization of cell energy
utilization^[76]26 (Equation [77]1). The rate of each reaction in the
model has a limit. For reversible reactions, the upper and lower bound
is set to 1000 and −1000 mmol/g[Biomass].h, respectively. For
irreversible reactions, the lower bound is set to zero. The COBRA
toolbox and the Gurobi 5 linear optimization algorithm were used for
FBA analysis in this study.
[MATH: Objectivefunction:max/minZ=CT∗v :MATH]
1
[MATH: Constraints:S∗v=0
:MATH]
2
[MATH: lb≤v≤ub
:MATH]
3
where S is a m × n sparse matrix, in which m refers to the number of
metabolites and n refers to the number of reactions. v represents the
rate vector of all reactions. lb and ub defines the lower and upper
bound of each reaction, respectively. In equation [78]1, C^T refers to
the coefficient of each metabolite in the objective function.
Intracellular metabolic fluxes were predicted by the parsimonious FBA
(pFBA)^[79]27. The model used in this study, A. niger GEMs iHL1210 ^5
that was updated recently by our laboratory, contains 1727 mass and
proton balanced reactions and 1210 ORFs (see Supplementary File [80]3).
Maximization of cell growth was set as the objective function and the
measured values of q[S], q[by-product], q[P], q[O2] and m[ATP] as
constraints during simulation for the fed-batch fermentations (see
Supplementary File [81]3). The prediction performance using iHL1210 was
assessed by comparing the predicted values of μ and q[CO2] with the
measured values.
Results
In order to investigate the global regulating mechanisms of cell
metabolism under oxygen-limited condition, the integrative analysis of
physiological phenotypic data, metabolomics, transcriptomics and
fluxomics was adopted in this work (Fig. [82]1).
Figure 1.
[83]Figure 1
[84]Open in a new tab
Framework of multi-omics integration analysis used in this work.
Quantitative analysis of physiological parameters
Mimicking the industrial production using A. niger, fed-batch
cultivations applying oxygen limited strategy were conducted in this
work. The fed-batch fermentation process could be initially divided
into 2 main phases, i.e. aerobic phase (0–20 h) and oxygen limited
phase (20–72 h) according to profiles of the dissolved oxygen
concentration (DO) (Fig. [85]2A). The changes in the profiles of oxygen
uptake rate (OUR), carbon dioxide emission rate (CER) and dry cell
weight (DCW) were determined by oxygen supply (OTR). When the oxygen
supply was limited, the specific growth rate (μ) quickly decreased
(Fig. [86]2H). Meanwhile, the OUR and CER decreased sharply to a stable
level (Fig. [87]2C and E). By-products analysis showed that organic
acids and polyols were slightly excreted by cell (see Supplementary
File [88]1, Fig. [89]S1). The detailed calculation showed that the
total carbon ratio of these by-products (Y[by-prodcuts/s]) is about 5%,
thus it can be concluded that most of carbon source fluxed into the
biomass, CO[2] and product. During the oxygen limited phase, in
contrast to the increased yield of glucoamylase (Fig. [90]2F), μ was
decreased continuously and lower than 0.02 h^−1 (Fig. [91]2H) at the
end of fermentation.
Figure 2.
[92]Figure 2
[93]Open in a new tab
Profiles of DO (A), dry cell weight (DCW) (B), CO[2] production rate
(CER) (C), glucoamylase enzyme activity (D), O[2] uptake rate (OUR)
(E), yield of glucoamylase per gram biomass (Y[PX]) (F), specific
oxygen uptake rate (q[O2]) (G) and specific growth rate (µ) (H) for A.
niger DS03043 with high glucoamylase production during fed-batch
cultivations.
Profiling of key metabolites in core carbon metabolism
In the first time, the pool sizes of 65 intracellular metabolites
(amino acids, organic acids, sugar phosphates, nucleotides and
coenzymes) from different fermentation phases were determined by
LC-MS/MS or GC-MS. As shown in Fig. [94]3A–C, the pool sizes of most
intracellular metabolites decreased sharply when A. niger entered into
the oxygen limited phase. However, it was also found that some amino
acids (like Tyr and Val) and organic acids (like SUCC and CIT) still
accumulated over time (Fig. [95]4). The accumulation of organic acids
was consistent with the high intracellular redox level under limited
O[2] supply^[96]10,[97]28.
Figure 3.
[98]Figure 3
[99]Open in a new tab
Data analysis of metabolomics at different fermentation phases (18 h,
24 h, 36 h, 48 h, 60 h, 72 h and 96 h. Heatmap of organic acid and
sugar phosphates (A), amino acids (B), nucleotide and coenzymes (C),
scores plot for samples in PCA analysis (D), loadings plot for
metabolites in PCA analysis (E) VIP score of 69 intracellular
metabolites calculated using PLS model (F). The detailed VIP scores for
each metabolites can be found in Supplementary File [100]2.
Figure 4.
[101]Figure 4
[102]Open in a new tab
Schematic representation of the changes in the pool sizes of organic
acids, sugar phosphates and amino acids during different fermentation
phases onto the core carbon metabolism network. All metabolite pool
sizes were determined in at least triplicate measurements. The
fermentation time (h) is on the x-axis and the metabolites
concentration (μmol/gDCW) is on the y-axis. The arrows in the small
gridlines represent the increase or decrease for the pool sizes of
intracellular amino acids during the oxygen limited phase compared to
that in oxygen excess phase. The detailed profiles of intracellular
amino acids pool sizes could be found in Supplementary File [103]1,
Fig. [104]S2.
The changes in intracellular amino acid pool sizes during the oxygen
limited phase exhibited two different tendencies (Figs [105]3B and
[106]4). The pool sizes of Ala, Gly, Asp, Glu and Ser decreased sharply
when the cells entered into the oxygen limited phase, while the pool
sizes of Val, Leu, Ile and His increased significantly (see
Supplementary File [107]1, Fig. [108]S2), consistent with the
extracellular accumulation of these amino acids (see Supplementary
File [109]1, Fig. [110]S3).
Furthermore, the principal component analysis (Fig. [111]3E) showed
that all the samples can be categorized into three groups, which is
difficult observed from physiological profiles (Fig. [112]2). On the
other hand, the relation between changes in pool sizes of metabolites
and q[O2] was studied by partial least squares (PLS) analysis. The VIP
of 45 metabolites was above 1 (Fig. [113]3F), indicating that the
changes of most intracellular metabolites concentration were sensitive
to the external environment perturbations.
Systematic analysis of gene and typical transcription factors (TFs)
expression pattern related to external environmental changes
The expression data of 10,445 genes from different fermentation phases
(16 h, 24 h, 42 h and 66 h) was determined using RNA-seq. According to
PCA analysis based on FPKM values of genes (Fig. [114]5B), it is shown
that the replicate samples from the same time point could be clustered
together. All the samples can be clustered into three groups,
consistent with that using metabolomics analysis. To obtain the main
metabolic characteristics of A. niger under oxygen limited environment,
the gene expression pattern analysis along the fed-batch process was
firstly carried out, followed by the gene set analysis of
differentially expressed genes in two distinct fermentation phases
(oxygen sufficient phase 16 h and oxygen limitation phase 42 h).
Figure 5.
[115]Figure 5
[116]Open in a new tab
Gene set analysis of differentially expressed genes in the aerobic
(16 h) and oxygen limited phases (42 h). Correlation coefficients of
samples (A), scores plot for samples in PCA analysis using gene
expression values (B), Gene set analysis based on GO function
annotation (C) and KEGG pathway enrichment analysis (D). The
non-directional class disregards the direction of change. The
distinct-directional class takes direction of change into account. The
mixed-directional class considers the up-regulated subset and the
down-regulated subset of a gene set separately. Each subset is scored
according to the proportion of significant genes.
With the aid of the Mfuzz package^[117]21 based on the R language,
those genes, with little expression (even no expression) at least two
or more time points during the fermentation, were firstly removed and
finally 6,662 genes were screened from the total 10,445 genes. Then the
cluster analysis of gene expression profiles was conducted for the
remaining 6,662 genes. The result showed that the expression pattern of
all 6,662 genes could be divided into 20 clusters (Fig. [118]6). Among
the 20 clusters, there exist several clusters in which the expression
of genes was consistent to the changes of q[O2] (Fig. [119]2G). For
example, the expression of 408 genes in cluster 8 decreased with the
decrease of q[O2], while in cluster 12, the expression of 373 genes
increased with the decrease of q[O2]. In addition, in cluster 2, the
expression of 592 genes decreased sharply from the aerobic phase (16 h)
to the transition phase (24 h), and remained stable in the mid and
later oxygen limitation phases (42 h and 66 h). In contrast, 201 genes
in cluster 6 showed an opposite tendency.
Figure 6.
[120]Figure 6
[121]Open in a new tab
Clusters of expressed genes during different fermentation phases. The
number of 1, 2, 3 and 4 in each small graph represents 16 h, 24 h, 42 h
and 66 h respectively.
DAVID, an online gene function annotation tool^[122]22, was exploited
to carry out gene function annotation and enrichment analysis of the
genes from the up-regulation group (cluster 12 and cluster 6)
(Table [123]1) and the down-regulation group (cluster 8 and cluster 2)
(Table [124]2). There were about 423 transcription factors (TFs) in A.
niger genome according to the Aspergillus Genome Database^[125]29. In
the above cluster analysis, the expression of TFs in different clusters
was extracted and studied. Cluster 12, where the gene expression was
continuously up-regulated, contained 10 TFs, among which flbA
(An02g03160, related to morphological development) and riaA
(An16g05550, related to NADPH oxidation regulation) were included.
Another TF, brlA (An01g10540), related to the formation of spores in
cluster 2, was down-regulated under oxygen limited condition. The
expression of 9 TFs in cluster 8 was significantly down-regulated.
Among them, An12g00130, which is thought to play a role in regulating
the mitochondrial respiratory chain complex IV biogenesis. Under oxygen
limited conditions, the cell could decrease the biosynthesis of complex
IV in the electron transport chain by down-regulating the expression of
An12g00130.
Table 1.
GO function enrichment analysis of genes from the up-regulation group.
Category GO Number Term P-Value
cluster 12
GOTERM_BP_FAT GO:0006913 nucleocytoplasmic transport 0.001
GOTERM_BP_FAT GO:0051169 nuclear transport 0.001
GOTERM_CC_FAT GO:0005643 nuclear pore 0.002
GOTERM_CC_FAT GO:0046930 pore complex 0.002
GOTERM_CC_FAT GO:0005635 nuclear envelope 0.002
GOTERM_BP_FAT GO:0046907 intracellular transport 0.024
GOTERM_CC_FAT GO:0031967 organelle envelope 0.029
GOTERM_CC_FAT GO:0012505 endomembrane system 0.030
GOTERM_CC_FAT GO:0031975 envelope 0.032
GOTERM_BP_FAT GO:0051170 nuclear import 0.034
GOTERM_BP_FAT GO:0034504 protein localization in nucleus 0.034
GOTERM_BP_FAT GO:0006606 protein import into nucleus 0.034
GOTERM_CC_FAT GO:0016021 integral to membrane 0.035
GOTERM_CC_FAT GO:0031224 intrinsic to membrane 0.040
GOTERM_MF_FAT GO:0004857 enzyme inhibitor activity 0.050
INTERPRO Hypoxia induced protein conserved region 0.057
GOTERM_BP_FAT GO:0017038 protein import 0.076
SP_PIR_KEYWORDS tricarboxylic acid cycle 0.077
GOTERM_BP_FAT GO:0006979 response to oxidative stress 0.084
GOTERM_MF_FAT GO:0004022 alcohol dehydrogenase (NAD) activity 0.088
cluster 6
INTERPRO Acyl-CoA oxidase/dehydrogenase, type1/2, C-terminal 0.012
INTERPRO Acyl-CoA oxidase/dehydrogenase, central region 0.014
GOTERM_MF_FAT GO:0003995 acyl-CoA dehydrogenase activity 0.015
GOTERM_MF_FAT GO:0009055 electron carrier activity 0.034
GOTERM_BP_FAT GO:0043087 regulation of GTPase activity 0.043
GOTERM_BP_FAT GO:0051336 regulation of hydrolase activity 0.043
GOTERM_MF_FAT GO:0008336 gamma-butyrobetaine dioxygenase activity 0.048
GOTERM_BP_FAT GO:0006091 generation of precursor metabolites and energy
0.077
GOTERM_MF_FAT GO:0030695 small GTPase regulator activity 0.095
[126]Open in a new tab
Table 2.
GO function enrichment analysis of genes from the down-regulation
group.
Category GO Number Term P-Value
cluster 8
GOTERM_BP_FAT GO:0006259 DNA metabolic process 0.009
GOTERM_BP_FAT GO:0033554 cellular response to stress 0.011
GOTERM_BP_FAT GO:0006260 DNA replication 0.013
GOTERM_MF_FAT GO:0000036 acyl carrier activity 0.014
GOTERM_MF_FAT GO:0016597 amino acid binding 0.014
GOTERM_MF_FAT GO:0043176 amine binding 0.014
GOTERM_BP_FAT GO:0006281 DNA repair 0.022
GOTERM_BP_FAT GO:0006974 response to DNA damage stimulus 0.023
GOTERM_MF_FAT GO:0004386 helicase activity 0.028
GOTERM_MF_FAT GO:0031177 phosphopantetheine binding 0.035
GOTERM_MF_FAT GO:0031406 carboxylic acid binding 0.049
GOTERM_MF_FAT GO:0048037 cofactor binding 0.050
GOTERM_BP_FAT GO:0019748 secondary metabolic process 0.067
GOTERM_BP_FAT GO:0051276 chromosome organization 0.089
GOTERM_MF_FAT GO:0016879 ligase activity, forming carbon-nitrogen bonds
0.096
GOTERM_MF_FAT GO:0004842 ubiquitin-protein ligase activity 0.097
cluster 2
GOTERM_BP_FAT GO:0030163 protein catabolic process 0.047
GOTERM_CC_FAT GO:0044430 cytoskeletal part 0.049
GOTERM_MF_FAT GO:0008171 O-methyltransferase activity 0.055
GOTERM_BP_FAT GO:0009057 macromolecule catabolic process 0.064
GOTERM_CC_FAT GO:0015630 microtubule cytoskeleton 0.067
GOTERM_MF_FAT GO:0000166 nucleotide binding 0.085
GOTERM_CC_FAT GO:0005856 Cytoskeleton 0.096
GOTERM_MF_FAT GO:0004386 helicase activity 0.097
GOTERM_MF_FAT GO:0016405 CoA-ligase activity 0.098
[127]Open in a new tab
The gene set analysis of differentially expressed genes at 42 h and
16 h was further performed by the Piano package^[128]23 based on the R
language (Fig. [129]5C,D). The results showed that in order to adapt to
the limited oxygen supply, the biosynthesis of fatty acids and
secondary metabolites, ribosome biogenesis and translation were
significantly down-regulated (P_value < 0.05), while the fatty acid
catabolism was up-regulated, which can further be validated by changes
in tendencies of genes expression from the related synthesis pathway
(see Supplementary File [130]1, Fig. [131]S6).
Flux simulation based on newly updated GEMs
To further investigate how the cell adapted to the external
environmental changes, the flux distribution predicted by pFBA was
exploited. The predicted μ and q[CO2] are consistent with the measured
values (see Supplementary File [132]3), indicating the good
performances of iHL1210. The flux simulation using iHL1210^[133]5
showed that the relative fluxes through the EMP pathway increased when
the cells entered into the oxygen limited phase (Fig. [134]7,
Supplementary File [135]3). Consistent with the increased relative flux
through EMP pathway, the expression of the corresponding genes was
maintained at a stable level, such as fructose-bisphosphate aldolase
(EC 4.1.2.13) and pyruvate dehydrogenase (EC 1.2.4.1, EC 2.3.1.12). In
addition, the pool sizes of some amino acids from the aromatic and
pyruvate families increased accordingly, which might provide precursors
for enzyme production.
Figure 7.
[136]Figure 7
[137]Open in a new tab
Trends of genes expression and relative flux of the EMP and PP and TCA
pathways under different fermentation phases. The red arrows represent
the reactions with an obvious increase in the relative flux predicted
by iHL1210 in the oxygen limited phase. The fermentation time (h) is on
the x-axis and the gene expression value (FPKM) is on the y-axis. The
black and red lines in each small graph represent independent isogenies
encoding the specific enzymes. The detailed flux distribution by pFBA
at different time points could be found in Supplementary File [138]3.
Flux simulation of GEMs iHL1210 showed that relative fluxes of the PP
pathway decreased slowly along the fermentation (see Supplementary
File [139]3), consistent with the reduction tendency in the gene
expression values for glucose-6-phosphate dehydrogenase (EC 1.1.1.49).
However, it was notable that gene expression of transketolase (EC
2.2.1.1) in the PP pathway was significantly up-regulated.
Consistent with higher pool sizes of organic acids, the flux simulation
with iHL1210 showed that the relative flux through the TCA cycle was
increased upon oxygen limitation, which was related to the lower demand
for the anabolic metabolism from the cell growth. To maintain the redox
balance, the cell metabolic activities were regulated in
transcriptional level. Firstly, the expression of genes encoding the
key enzymes of the TCA cycle, including citrate synthase (EC 2.3.3.1)
and aconitate hydratase (EC 4.2.1.3), were down-regulated. Meanwhile,
the expression of genes (EC 2.3.3.9 and EC 2.6.1.19) from the
glyoxylate and GABA cycles were significantly up-regulated
(Fig. [140]7). Consistent with this observation, the flux simulation
indicated that the relative flux through the glyoxylate cycle increased
(see Supplementary File [141]3).
Discussion
Cell metabolism based on gene expression pattern analysis under oxygen
limited conditions
According to the cluster analysis in the Results section, there exist
two obvious gene expression patterns, namely up-regulation (cluster 12
and cluster 6) and down-regulation (cluster 8 and cluster 2). Under
oxygen limited conditions, the energy supply could become a main
bottleneck for complex cell metabolic functions including membrane
synthesis. The enrichment analysis of genes in cluster 6 revealed that
the energy production was up-regulated during the oxygen limited phase,
helping to relieve the shortage in energy supply. Genes in cluster 8
were enriched in the biological processes closely related to DNA
unwinding, replication and transcription, indicating that oxygen
limitation could weaken the transcription and translation to reduce
energy demand. Parts of the genes in cluster 2 were also enriched in
DNA unwinding, indicating that the DNA replication was obviously
weakened, which was highly consistent with the decreased specific
growth rate during the oxygen limited phase (Fig. [142]2H). Meanwhile,
the synthesis of macromolecules slowed down and the expression of genes
encoding degradation pathways of macromolecules (like proteins) was
up-regulated accordingly (cluster 2) to strengthen the turnover of
intracellular metabolites. According to the genome annotation^[143]29,
transcription factor flbA in cluster 12 is the regulator of the
G-protein signaling protein. It has been reported that the deletion of
this gene displayed the phenotype of long thin mycelium without
arthrospore, and promoted protein secretion^[144]30. In this study, the
expression of flbA increased continuously, which might thicken the cell
wall and hinder the protein secretion. Sterol regulatory
element-binding proteins (SREBPs) are important TFs for filamentous
fungi to adapt to an anaerobic environment as the decrease of the
intracellular sterol concentration in anaerobic environment will
activate the expression of SREBPs^[145]31. In A. niger, the
corresponding genes are srbA (An03g05170) and srbB (An14g02540),
respectively^[146]32. In this work, the expression of the former was
down-regulated first and then was up-regulated (in cluster 14) while
the expression of the latter was continuously elevated (in cluster 12),
indicating that SREBPs are conserved for fungi to adapt to the oxygen
limited environment.
Potential advantages of the oxygen limited strategy used for enzyme
production by A. niger
Similar to P. pastoris^[147]17, an appropriate oxygen limitation favors
glucoamylase production by A. niger. However, the detailed mechanisms
behind this phenomenon are not clear. Generally, there is an inverse
correlation between protein production and cell growth^[148]33 as the
protein synthesis usually needs the competitive precursors for the cell
growth. During the oxygen limited phase, μ was lower than 0.02 h^−1
(Fig. [149]2H). It could be speculated that the low specific growth
rate can be a possible reason of high yield of glucoamylase due to the
fact that once the growth was limited, more NADPH, NADH and precursors
could be fluxed into synthesis of glucoamylase. Especially, the
increased relative flux in the EMP pathway, along with the accumulation
of amino acids from the pyruvate and aromatic families, is beneficial
for protein synthesis. Furthermore, the transcription factors relating
to sterol synthesis, one important composition of the cell membrane,
was activated during the oxygen limitation, which possibly helped to
maintain the integrity of the cell membrane^[150]14, favoring an
efficient secretion of glucoamylase.
It has been reported that the exogenous addition of the limited amino
acids could effectively alleviate the shortage in supply of amino
acids, energy and reducing power^[151]34. In this work, the addition of
Ala, Gly, Asp, Glu and Ser could obviously promote the enzyme
production (see Supplementary File [152]1, Fig. [153]S4).
Coincidentally, these amino acids have a decreased tendency in
intracellular pool sizes along with the fermentation. In combination
with the fact that the four amino acids are the main compositions of
glucoamylase (Table [154]3), it can be inferred that they might be the
limiting precursors for the synthesis of the target protein, which
provides new clues for metabolic engineering to promote enzyme
production efficiency using molecular biology. It should be also noted
that some amino acids, like His, Lys, Val, Ile, etc., were accumulated
within the cell. For the accumulation of aromatic amino acids and part
of the pyruvate family, a driver could be the increased flux through
the EMP pathway. Moreover, the pool sizes of amino acids were closely
related to the changes in the expression levels of genes from the
corresponding pathways. Taking Ala and Gly as an example, the decrease
in pool sizes of these two amino acids was accompanied with the
observation of a prominent decrease in gene expression levels of the
Ala and Gly synthesis pathways (see Supplementary File [155]1,
Fig. [156]S5), initially indicating that the pool sizes of amino acids
were rigidly controlled. In terms of energy requirements, Lys, Met,
Ile, Trp and His can be regarded as expensive amino acids^[157]35. It
is reported that under environmental stress conditions the cell could
secure some of the expensive amino acids while at the same time
decreasing the pool sizes of the cheap amino acids^[158]36. As a
result, when the environment changes again to more favorable
conditions, the cell could realize fast growth by mobilizing the energy
expensive amino acids. The precise mechanisms for the amino acids
accumulation under oxygen limited conditions still need further
experimental validation.
Table 3.
Amino acid composition of glucoamylase.
animo acid content amino acid content
Ala 10.17% Met 0.47%
Cys 1.56% Asn 3.91%
Asp 6.89% Pro 3.44%
Glu 3.91% Gln 2.66%
Phe 3.44% Arg 3.13%
Gly 7.20% Ser 13.77%
His 0.63% Thr 11.58%
Ile 3.76% Val 6.57%
Lys 2.03% Trp 3.13%
Leu 7.51% Tyr 4.23%
[159]Open in a new tab
The possible mechanisms for high yield of glucoamylase during the
oxygen limited phase are summarized in Fig. [160]8.
Figure 8.
[161]Figure 8
[162]Open in a new tab
Possible coordinated regulation mechanisms for maintenance of the redox
(energy) balance, as well as for the high enzyme production by A. niger
upon oxygen limitation. The contents in red and green frames represent
the up- and down-regulated metabolic activities, respectively.
How the cell maintains redox and energy balance under hypoxic conditions?
Under hypoxic conditions, the ways for different species to maintain
intracellular energy and redox balance may be different. Unlike fungal
strains, S. cerevisiae and mammalian cells could regenerate NADH via
the formation of ethanol and lactic acid to maintain the intracellular
energy and redox balance. With ^13C labeling experiments, the P/O ratio
of S. cerevisiae was still found high under hypoxic conditions^[163]37,
which indicates that the oxidative phosphorylation remained efficient.
A high formation rate of ATP helps to promote the oxidative
phosphorylation under hypoxic conditions^[164]38. Also, the oxygen
limitation conditions could reduce leakage of protons and uncoupled
respiration^[165]38. In this work, the marginal secretion of polyols
and reduced organic acids was not enough to sustain the regeneration of
NAD^+. Therefore, it could be speculated that, similar to A.
fumigatus^[166]13, oxidative phosphorylation is the main metabolic
pathway for maintaining the intracellular balance of energy and redox.
The transcriptomics and flux simulation indicated that the flux through
the glyoxylate cycle was increased, reducing NADH formation from the
TCA cycle, which helps to maintain the redox balance. In addition, as
reported in the literature, during oxygen limited conditions, the
strain could maintain the redox balance by the reduction of nitrate, as
well as the secretion of branched amino acids^[167]39. As ammonium
sulfate was used as nitrogen source in the work, the reduction of
nitrate could be excluded. As for the secretion of branched amino
acids, like Val, Ile, Leu, etc., they are slightly accumulated within
the cell and the extracellular secretion was marginal, although still
insufficient to maintain the intracellular redox balance. Therefore, we
concluded that mainly the enforcements of glyoxylate cycle (the GABA
shunt is also possible) and oxidative phosphorylation help to maintain
the intracellular redox balance.
On the other hand, the oxygen limitation could lead to the shortage in
supply of ATP and NADPH. To maintain the cell normal metabolic
activities, the cell can reduce anabolism and strengthen the
catabolism, which was validated by a decrease in the gene expression
levels of the fatty acid synthesis pathway, as well as the increase in
gene expression levels of the fatty acid catabolism pathway. The
enforcement of the EMP pathway could increase the formation of ATP,
alleviating the energy demand. As for NADPH supply, there are mainly
three sources according to transcriptomics data: the PP pathway,
ICIT + NADP = > AKG + NADPH and MAL + NADP = > PYR + NADPH. The gene
expression levels of the former two sources were decreased while they
were increased in the third source. With ^13C labeling flux
analysis^[168]40, it was found that in the A. niger high-producing
strain, the flux through MAL + NADP = > PYR + NADPH was higher than
that in the wild type strain. So it could be concluded that under
oxygen limited conditions, MAL + NADP = > PYR + NADPH might be a
potential gene target for metabolic engineering to provide more NADPH
supply. The supposed metabolic regulation mechanisms for the cell to
maintain the intracellular redox and energy balance can be found in
Fig. [169]8.
Conclusion
The multi-omics integrative analysis provides us new insights on the
mechanisms of A. niger metabolic regulation under fed-batch process
conditions for enzyme production. To maintain the intracellular redox
and energy balance under hypoxic condition, the cell metabolism was
regulated at different aspects. The pool sizes of most intermediate
metabolites from the upper EMP and PP pathways decreased along the
fermentation. Meanwhile the gene expression was reduced for the fatty
acid and ribosome synthesis pathways accordingly to weaken the cell
anabolic metabolism. On the contrary, the EMP pathway and glyoxylate
pathway were activated, which can be validated by the association
analysis of transcriptomics and fluxomics. The possible reasons for a
high yield of glucoamylase during the oxygen limited phase can be
summarized as follows. Firstly, the increased relative flux through the
EMP pathway could provide more precursors for enzyme synthesis.
Secondly, the down regulations in fatty acid and ribosome biogenesis
could also channel more precursors towards glucoamylase synthesis.
Thirdly, the up-regulation in gene expression for sterols synthesis
might favor the enzyme secretion. The multi-omics integrative analysis
illustrated, in a systematic view, the potential of an oxygen limited
strategy used in the industrial fed-batch fermentation. Furthermore, a
rational optimization of the A. niger metabolic network in terms of
precursors and NADPH supply, as well as re-balancing the NADH and
sterol biosynthesis may further help A. niger adapt to hypoxic
conditions, as well as improvement of the enzyme productivity.
Electronic supplementary material
[170]Supplementary file 1^ (1.1MB, pdf)
[171]Supplementary file 2^ (17.5KB, xlsx)
[172]Supplementary file 3^ (2.4MB, xls)
Acknowledgements