December 2018⎪Vol. 28⎪No. 12
J. Microbiol. Biotechnol. (2018), 28(12), 1971–1981https://doi.org/10.4014/jmb.1809.09055 Research Article jmbReview
Varying Inocula Permutations (Aspergillus oryzae and Bacillusamyloliquefaciens) affect Enzyme Activities and Metabolite Levels inKojiHye Jeong Gil†, Sunmin Lee†, Digar Singh, and Choong Hwan Lee*
Department of Bioscience and Biotechnology, Konkuk University, Seoul 05029, Republic of Korea
Introduction
Fermentation is a serial biochemical process in which
organic substrates are transformed by microbial enzymes,
followed by the production of commercially important
metabolic flux [1]. Customarily, food fermentation is
considered an important method for producing items with
longer shelf life, better taste, and improved nutritional as
well as functional properties compared to the raw
materials [2]. Among traditional Korean fermented foods,
meju (fermented soybean block) has been primarily used as
the major starter ingredient of various fermented food
products, including doenjang (fermented soybean paste),
ganjang (fermented soy sauce), and gochujang (fermented
hot-pepper paste). The artisanal meju fermentation induces
spontaneous colonization by various microflora, including
fungi, bacteria, and yeast species, which hydrolyze major
substrate components (proteins, lipids, and carbohydrates)
to different metabolites such as organic acids, amino acids,
and functional metabolites [3]. However, the artisanal meju
production methods are associated with certain quality
control complications owing to the unregulated and poorly
characterized colonization by microbial communities [4, 5].
It has been proposed that koji fermented with characterized
inocula act as better starter ingredients for food fermentative
bioprocesses [6]. Similarly, koji (squashy fermented rice/
soybean) represents yet another starter ingredient, which
additionally delivers the catalytic enzyme as well as
Received: September 28, 2018
Revised: October 10, 2018
Accepted: October 11, 2018
First published online
October 19, 2018
*Corresponding author
Phone: +82-2-2049-6177;
Fax: +82-2-455-4291;
E-mail: [email protected]
†These authors contributed
equally to this work.
upplementary data for this
paper are available on-line only at
http://jmb.or.kr.
pISSN 1017-7825, eISSN 1738-8872
Copyright© 2018 by
The Korean Society for Microbiology
and Biotechnology
In this study, we investigated the altered enzymatic activities and metabolite profiles of koji
fermented using varying permutations of Aspergillus oryzae and/or Bacillus amyloliquefaciens.
Notably, the protease and β-glucosidase activities were manifold increased in co-inoculated
(CO) koji samples (co-inoculation of A. oryzae and B. amyloliquefaciens). Furthermore, gas
chromatography-mass spectrometry (GC-MS)-based metabolite profiling indicates that levels
of amino acids, organic acids, sugars, sugar alcohols, fatty acids, nucleosides, and vitamins
were distinctly higher in CO, SA (sequential inoculation of A. oryzae, followed by
B. amyloliquefaciens), and SB (sequential inoculation of B. amyloliquefaciens, followed by
A. oryzae). The multivariate principal component analysis (PCA) plot based on GC-MS
datasets indicated a clustered pattern for MA and MB (koji samples inoculated either with A.
oryzae or B. amyloliquefaciens) across PC2 (20.0%). In contrast, the CO, SA, and SB metabolite
profiles displayed segregated patterns across PLS1 (22.2%) and PLS2 (21.1%) in the partial
least-square discriminant analysis (PLS-DA) model. Intriguingly, the observed disparity in the
levels of primary metabolites was engendered largely by higher relative levels of sugars and
sugar alcohols in MA, SA, and CO koji samples, which was commensurate with the relative
amylase activities in respective samples. Collectively, the present study emphasizes the utility
of integrated biochemical and metabolomic approaches for achieving the optimal permutation
of fermentative inocula for industrial koji preparation.
Keywords: Koji fermentation, Aspergillus oryzae, Bacillus amyloliquefaciens, co-inoculation,
sequential inoculation
S
S
1972 Gil et al.
J. Microbiol. Biotechnol.
bioactive metabolites, remarkably affecting the end-product
quality [7]. A variety of substrates, including rice, barley,
wheat, and soybean, have been used for fermentative koji
preparation [8, 9].
The quintessential koji fermentation bioprocess involves
a variety of microbial species, including fungi such as
Aspergillus (A. oryzae, A. sojae, and A. awamori), Actinomucor
taiwanensis, Rhizopus, and yeast (Zygosaccharomyces rouxii,
and Saccharomyces cerevisiae). In addition, various bacterial
species such as Bacillus (Bacillus amyloliquefaciens, B. subtilis,
and B. natto) and lactic acid bacteria (LAB), including
Tetragenococcus halophilus have been previously used [8,
10-12]. Especially, Bacillus genus strains are predominant
bacteria during the early stages of the doenjang-meju
fermentation process and Aspergillus genus strains become
dominant as the fermentation progresses with changing
environmental conditions [3, 5].
Rice koji fermented with Aspergillus species has been
used for the production of doenjang, sake, soy sauce, and
certain vinegars. Rice koji provides sugars for growth, and
subsequent fermentation through saccharification provides
sweetness to rice-based alcoholic beverages [13]. A. oryzae
(the koji mold) is considered a “generally regarded as safe”
(GRAS) mold and has been revered for centuries for koji-
making owing to its ability to secrete abundant hydrolytic
enzymes, including amylases, proteases, lipases, and
cellulases, coupled with production of a wide range of
functional metabolites and their derivatives with anti-
tumor, antibacterial, and antioxidant properties [7, 14, 15].
In contrast, B. amyloliqeufaciens is widely used for
fermentative koji preparation owing to its high growth rate
and ability to produce fibrinolytic enzymes [7, 16]. Recently,
various studies have suggested improving koji quality using a
single strain or inocula, whereas others have suggested using
enhanced enzymatic blends and nutritional components of
fermented foods inoculated with multiple strains. In this
context, Kim et al. have reported the remarkably distinct
titratable acidities and amino-type nitrogen contents of
gochujang made using different inocula permutations of
A. oryzae and B. subtilis [17]. Similarly, Singracha et al. have
reported alterations in the microbiological and biochemical
properties of soy sauce fermented with various yeasts
(Zygosaccharomyces rouxii, Meyerozyma guilliermondii) and
LAB (T. halophilus) species [18]. However, comprehensive
studies on koji fermentation using mixed strains are limited
owing to meager understanding of strain-specific microbial
interactions in fermented foods [19].
Metabolomics has been considered a useful tool for
evaluating the nutritional and functional values of
fermented foods and microbial interactions at the molecular
level as it aims to monitor all metabolites, which are
intermediates or end products of metabolic pathways [20, 21].
Previously, we have reported that time-resolved metabolite
profiles of rice koji fermentative bioprocess are affected by
different substrates and strain inocula [8, 21]. In this study,
we investigated rice koji fermentation as a function of
interactions between filamentous fungi (A. oryzae) and
bacteria (B. amyloliquefaciens) in co-cultures of different
permutations of these microbes based on enzymatic
activities and mass spectrometry (MS)-based metabolomic
profiles to improve the nutritional qualities of rice koji.
Materials and Methods
Chemicals and Reagents
All chemicals were of analytical grade and purchased from
Sigma-Aldrich (USA), Junsei Chemical Co. Ltd. (Japan), or Fisher
Scientific (USA).
Microbial Cultures and Koji Fermentation
Rice of a Korean cultivar, ‘Jinsang’, was used in this study. The
rice used had 70% embryo bud and bran layer, which was achieved
by milling using a polishing machine (model MP-220, Yamamoto
Co., Japan). The two inocula types i.e., A. oryzae (KCCM 12698) and
B. amyloliquefaciens (KCCM 43033) were procured from the Korean
Culture Center of Microorganisms (KCCM, Korea). A. oryzae was
maintained on malt extract agar (malt extract, 20 g; glucose, 20 g;
peptone, 1 g; agar, 20 g/l) at 28°C and B. amyloliquefaciens was
maintained on Luria Bertani (LB) broth with agar (tryptone 10 g,
NaCl 10 g, yeast extract 5 g, and agar, 12 g/l) at 30°C. Rice koji was
prepared following the steps shown in Fig. 1. First, rice was soaked
in four times its volume of distilled water for 12 h, drained, and
steamed at 121°C for 30 min. After cooling, the cooked rice was
inoculated with different inocula permutations to make the
following koji sets: MA - monoculture, A. oryzae; MB - monoculture,
B. amyloliquefaciens; CO - co-culture, A. oryzae and B. amyloliquefaciens;
SA - sequential inoculation, B. amyloliquefacines after 36 h of
A. oryzae inoculation, and SB - sequential inoculation, A. oryzae
after 36 h of B. amyloliquefacines inoculation. All the koji sets were
incubated at 28°C for 72 h and the samples were harvested at 0,
36, and 72 h. The harvested samples were immediately stored at
deep-freezing conditions (-80°C) until further analyses.
Enzyme Activity Assay
Each rice koji sample (10 g) in 90 ml water was extracted by
shaking in an incubator at 120 rpm and 30°C for 1 h. The enzymatic
activity assays, including amylase, protease, and β-glucosidase
assays, were performed using filtered supernatants following the
method of Lee et al. [7].
Evaluating Koji with Varying Inocula 1973
December 2018⎪Vol. 28⎪No. 12
Amylase
Amylase activity of koji was determined using a 1% (w/v)
starch solution. The assay was conducted per the following
protocol. Equal amounts of koji extract supernatant and starch
solution (1 ml) were mixed and incubated at 55°C for 10 min. The
reaction was terminated using dinitrosalicylic acid (DNS) solution
(1 ml) and boiling at 100°C for 15 min. After cooling to room
temperature for 3 min, 9 ml pure water was added to the reaction
mixture. The absorbance of the reaction mixture was observed at
540 nm using a spectrophotometer.
β-Glucosidase
The β-glucosidase activity of the koji samples was determined
using the substrate p-nitrophenyl β-D-glucopyranoside (pNPG).
The assay was conducted as follows. The supernatant (1 ml) of koji
extract, 9 mM pNPG (1 ml), and sodium acetate buffer (8 ml) were
mixed and incubated at 37°C for 30 min. The reaction was terminated
using 0.4 M sodium carbonate (5 ml). The absorbance of the reaction
mixture was observed at 400 nm using a spectrophotometer.
Protease
Protease activity of koji was determined using casein solution as
the substrate. The assay was conducted as follows. Supernatant
(1 ml) of koji extract and casein solution (5 ml) were mixed and
incubated at 37°C for 10 min. The reaction was terminated using
0.4 M trichloroacetic acid (5 ml) and incubating at 37°C for 30 min,
followed by filtration. The filtrate (2 ml), Folin’s solution (1 ml),
and 0.4 M sodium carbonate (5 ml) were mixed and incubated at
37°C for 30 min. After incubation, the absorbance of the reaction
mixture was observed at 660 nm using a spectrophotometer.
Gas Chromatography-Time-of-Flight Mass Spectroscopy (GC-
TOF-MS) Analysis
Rice koji (3 g) was soaked in 30 ml methanol/water (80/20) and
sonicated for 10 min. After sonication, the metabolites were
extracted by shaking for 24 h at room temperature. For GC-TOF-
MS analysis, the derivatization steps were performed as described
by Lee et al. [6]. The GC−TOF−MS instrumentation included an
Agilent 7890A GC System (USA) and a Pegasus HT TOF-MS (Leco
Corporation, USA) with A RTx-5MS (30 m length × 0.25 mm inner
diameter, J & W Scientific, USA). The operational conditions were
maintained as follows: carrier gas (helium) flow rate of 1.5 ml/min,
injector temperatures at 250°C, ion source temperatures at 230°C,
sample injection volume of 1 μl, split ratio of 1:15, and mass scan
range (m/z) between 45-1,000. The oven temperature was initially
maintained at 75°C for 2 min and then increased to 300°C at the
rate of 15°C/min, and sustained for 3 min.
Data Processing and Multivariate Statistical Analysis
The raw data sets from GC−TOF-MS analysis were transformed
to netCDF (*.cdf) format using Leco ChromaTOF. Data in the
respective netCDF (*.cdf) files were processed using the MetAlign
software (http://www.metalign.nl)as described previously by
Lee et al. [6]. Multivariate statistical analyses were performed
using the SIMCA-P+ 12.0 software’s principal component analysis
(PCA) and partial least-squares discriminant analysis (PLS-DA),
which explains the differences in metabolic patterns among the
experimental groups. Variables with variable importance in the
projection (VIP) value > 0.7 were selected. Significance was
calculated using one-way analysis of variance (ANOVA),
Student’s t-test, and Duncan’s multiple comparison test using
PASW Statistics 18.0 (SPSS Inc., USA). Metabolites were identified
by comparing the m/z, mass fragment, and retention times of the
samples with those of standard compounds using in-house library
and databases, including the National Institute of Standards and
Technology (NIST, version 2.0, 2011; FairCom, Gaithersburg, MD)
and Wiley 8 databases. The correlation between phenotypes and
metabolites were calculated based on Pearson’s correlation
coefficient using PASW Statistics 18.0, and the correlation maps
were constructed using the MEV software version 4.8.
Results
Enzyme Activities of Koji Samples Fermented Using
Different Inocula Permutations
The crude extracts from different koji types (MA, MB,
Fig. 1. Schematic representation of experimental procedures.
1; Inoculation time, A; Inocula size of Aspergillus oryzae (3 × 107
spores/ml), B; Inocula size of Bacillus amyloliquefaciens (2.3 × 109
CFU/ml).
1974 Gil et al.
J. Microbiol. Biotechnol.
CO, SA, and SB) were used for protease, β-glucosidase, and
amylase activity assays (Fig. 2, Table S1). Koji fermented
with Aspergillus (MA, SA, and CO) showed a gradual
increase in protease activity during fermentation, whereas
samples fermented with Bacillus (MB and SB) displayed
reduced activity after 36 h. However, all koji types displayed
a temporal increase in β-glucosidase activity irrespective of
the inocula permutations. Similarly, the amylase activities
for all koji types increased linearly with time with the
exception of the CO inocula set. Altogether, the protease
(157.7 U/g of koji) and β-glucosidase (9.0 U/g of koji)
activities were highest in CO, whereas higher amylase
activity was observed in koji fermented with Aspergillus
(MA; 0.74 U/g of koji, SA; 0.72 U/g of koji) at 72 h.
Multivariate Analysis of Primary Metabolite Profiling
Datasets of Koji Samples Fermented Using Different
Culture Systems
The distinct primary metabolite profiles of each koji, which
depended on the inocula permutations, were evaluated
using multivariate statistical analyses based on the GC-
TOF-MS datasets. The PCA and PLS-DA score plots
indicated a clustered pattern for different koji metabolite
profiles based on varying inocula permutations and
fermentation times across PC1 (23.5%) and PC2 (20.0%),
and PLS1(22.2%) and PLS2 (21.1%) as shown in Fig. 3.
Fig. 2. Changes in (A) protease, (B) β-glucosidase, and (C)
amylase activities, of koji samples made with varying inocula
permutations.
(MA - monoculture, A. oryzae, ▲ ; MB - monoculture, B.
amyloliquefaciens, ▼ ; CO - co-culture, A. oryzae and B. amyloliquefaciens,
◆ ; SA - sequential inoculation, B. amyloliquefacines after 36 h of
A. oryzae inoculation, ● ; SB- sequential inoculation, A. oryzae after
36 h of B. amyloliquefacines inoculation, ■ ).
Fig. 3. Gas chromatography time-of-flight mass spectroscopy
(GC-TOF-MS) datasets of primary metabolites.
The datasets for raw substrates are indicated - *, rice. Datasets for
A. oryzae-fermented samples - ▲ ; MA36 (harvested at 36 h), △; MA72
(harvested at 72 h). Datasets for B. amyloliquefaciens-fermented samples
- ▼ ; MB36 (harvested at 36 h), ▽ ; MB72 (harvested at 72 h). Datasets
for A. oryzae and B. amyloliquefaciens co-fermented samples - ◆ ; CO36
(harvested at 36 h), ◇ ; CO72 (harvested at 72 h). Datasets for
A. oryzae and B. amyloliquefaciens co-fermented samples using sequential
inoculation - ●; SA36 (harvested at 36 h), ○; SA72 (harvested at 72 h),
■ ; SB36 (harvested at 36 h), □ ; SB72 (harvested at 72 h).
Evaluating Koji with Varying Inocula 1975
December 2018⎪Vol. 28⎪No. 12
Variations in Relative Metabolite Abundance among Koji
Samples Prepared Using Varying Inocula Permutations
In total, 57 primary metabolites were significantly
discriminant (VIP > 0.7, p < 0.05) among different koji
samples (Table 1). These included 14 amino acids, 13
organic acids, 18 sugar and sugar alcohols, 7 fatty acids, 3
nucleosides, 1 inorganic acid, and 1 vitamin among different
koji types depending on varying inocula permutations and
fermentation time. The differential variables of each koji
(MA, MB, CO, SA, and SB) were selected based on the
variable importance in projection (VIP > 0.7) values as
indicated in Table 1, determined using the PLS-DA model
(Fig. 3B). Notably, the concentration of most primary
metabolites in rice koji with Aspergillus culture system (MA
and SA) increased with fermentation time, with the
exception of certain fatty acids, including palmitic acid
(47), linoleic acid (48), oleic acid (49), linolenic acid (50),
and oleamide (52). However, in simultaneous co-inoculation
culture system (CO), the levels of all detected primary
metabolites, with the exception of pyruvic acid (15) and
ferulic acid (26), increased with fermentation time.
Furthermore, the relative abundance of most amino acids,
except threonine (6), pyroglutamic acid (8), and GABA (9),
as well as those of fatty acids and nucleosides, were
remarkably increased in Aspergillus inoculated (SA) koji
samples after 36 h. In contrast, the levels of most organic
acids, except pyruvic acid (15), succinic acid (20), and
caffeic acid, (27), as well as those of fatty acids, with the
exception of oleamide (52), and nucleosides increased after
36-72 h, whereas the concentration of most amino acids
except ornithine (13) decreased in the MB culture system.
Furthermore, we analyzed the relative abundance of
significantly discriminant metabolites at 72 h and their
biosynthetic routes among different koji types made with
varying inocula permutations (MA, MB, CO, SA, and SB)
using the Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathway maps and published studies (Fig. 4).
Intriguingly, the relative abundance of most of the
metabolites was relatively higher in koji samples made with
co-culture inocula (CO, SA, and SB) than monoculture (MA
Fig. 4. Scheme of the primary metabolic pathway in koji fermented after 72 h with varying inocula permutations.
The pathway was adopted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg) and references.
Metabolite in gray font indicated not-detected compounds.
1976G
il et al.
J. Microbiol. Biotechnol.
Table 1. Variations in the relative abundance of primary metabolites among fermented culture system based on GC-TOF-MS data.
Evaluating Koji with Varying Inocula
1977
Decem
ber 2018⎪
Vol. 28⎪
No. 12
Table 1. Continued.
aRetention time.bm/z value of the selected ion for identification and quantification.
cNumber of trimethylsilyl groups.dThe relative contents of metabolites were represented as peak area transformed by log10. Mean (n = 3) ± standard deviation.
ND: Not detected * The color values (blue-to-red) represent fold change of each metabolite.
GC TOF-MS: Gas chromatography time-of-flight mass spectroscopy.
1978 Gil et al.
J. Microbiol. Biotechnol.
and MB) at 72 h. Notably, the concentration of the majority
of primary metabolites was highest in SA-inoculated koji at
72 h. These included metabolites associated with amino
acid and fatty acid metabolism, phenylalanine (11) and
tryptophan (14) syntheses via the shikimate pathway,
organic acids (succinic acid (20), fumaric acid (21), malic
acid (22), and citric acid (24)) of the tricarboxylic acid
(TCA) cycle, hydrocinnamic acid synthesized via the
phenylpropanoid pathway, sugar and sugar derivatives, as
well as certain fatty acids. On the contrary, SB-inoculated
koji at 72 h displayed significantly higher levels of several
amino acids, sugars, and sugar derivatives associated with
fatty acid metabolism compared to MB-inoculated koji. CO-
inoculated koji was characterized by higher protease and
β-glucosidase activities, and the relative abundance of
various sugar and sugar derivatives, hydrocinnamic acid,
citric acid (24), and stearic acid (51) compared to mono-
culture (MA and MB)-inoculated koji.
Correlation between Enzyme Activity and Metabolite
Composition of Koji
Statistical correlation between enzyme activities and
significantly discriminant metabolites in various koji types
prepared using inocula permutations were derived using
Pearson’s correlation analysis (Fig. 5). The coefficients of
enzyme activity (amylase, β-glucosidase, and protease) and
the relative abundance of the 57 metabolites were
represented by their color-plotted values (-1 < r < 0; red, 0 <
r <1; blue). As indicated in the correlation map, overall 42,
24, and 47 metabolites correlated positively with protease,
β-glucosidase, and amylase activities, respectively, in an
overlapping manner. Notably, six metabolites, including
citric acid (24), glycerol (28), glucose (37), myo-inositol (42),
lactose (44), and maltose (45), with Pearson’s correlation
coefficient higher than 0.7 (p < 0.05), correlated positively
with amylase activity, whereas five metabolites, including
coumaric acid (25), glycerol (28), glucose (37), dulcitol (38),
and stearic acid (54) displayed strong positive correlation
with protease activity.
Discussion
The present study delineates quintessential koji fer-
mentation as a function of microbial interactions in various
inocula permutations, including monoculture, co-culture,
and sequential culture of two strains (A. oryzae and
B. amyloliquefaciens). We utilized both biochemical (enzymatic
assays) and high-throughput, MS-based metabolomic
approaches in a correlative manner to unravel the effects of
Fig. 5. Correlation map between enzyme activities (protease,
β-glucosidase, and amylase) and metabolites.
Each square indicates the Pearson’s correlation coefficient values (r).
Red color represents positive (0 < r < 1) correlation, whereas blue
color represents negative (−1 < r < 0) correlation.
Evaluating Koji with Varying Inocula 1979
December 2018⎪Vol. 28⎪No. 12
varying inocula on koji end products. Considering the
importance of high enzyme-producing culture systems for
koji fermentation, we focused on the protease, β-glucosidase,
and amylase activities in different koji systems (Fig. 2). We
speculated that the varying levels of hydrolytic enzymes in
koji systems treated with different inocula permutations
might significantly affect the corresponding metabolite levels.
Proteases function as hydrolytic biocatalysts for micro-
organisms digesting large-sized protein macromolecules
into small-size peptides, which accelerate fermentation.
Herein, the proteases produced by the two strains
(Aspergillus and Bacillus) appeared to have synergistic
effects, resulting in additive protease activity for CO and
SA inocula permutations. In particular, the A. oryzae strain
harboring diverse protease-encoding genes secretes a wide
spectrum of proteolytic enzymes, including aminopeptidases,
serine endopeptidases, and aspartic endopeptidases, which
might have contributed to the higher protease levels in MA
than in MB [22, 23]. β-glucosidases hydrolyze glucose and
other sugar moieties from complex cellulose-rich substrates,
which possibly support microbial colonization in a
fermentative environment [24]. However, the differential
expression of β-glucosidases among different microbial
species is intricately regulated both by the available carbon
sources as well as by culture conditions [25]. β-Glucosidase
activities were distinctly higher in CO and SB koji types
owing to rapid substrate modulation by Bacillus species. In
contrast, amylase is a ubiquitous secretory enzyme that
catalyzes the hydrolysis of glycosidic bonds in poly-
saccharides. On an industrial scale, both B. amyloliquefaciens
and A. oryzae are used for amylase production [26].
Surprisingly, the amylase activity was relatively lower in
co-culture systems (CO) than in Aspergillus sequential (SA)
and mono-culture (MA) koji, which might be attributed to
the extensive mycelial growth that reportedly inhibits
extracellular amylase production [27].
According to the multivariate analyses based on GC-
TOF-MS metabolite profiling datasets, rice koji were
distinguished on the basis of fermentation time as well as
variations in inocula permutations (Figs. 3 and 4). In
particular, the metabolite profiles for CO koji types
represented a clustering pattern similar to that of Aserpgillus
koji (MA, SA) until 36 h, followed by a distinct pattern
between Aspergillus (MA, SA) and Bacillus (MB, SB) at 72 h,
signifying their temporal and inocula-dependent metabolomic
properties. Furthermore, considering the relative levels of
significantly discriminant primary metabolites among the
different koji types, we detected generally higher relative
abundance of most primary metabolites at 36 h (Table 1). In
agreement with the results of previous studies, we
observed a temporal variation in koji metabolomes, where
the microbial inocula and their concomitant enzymatic
activities subtly affect end product quality [6-8, 13]. The
relative levels of primary metabolites in rice koji can be
directly associated with its nutritional as well as organoleptic
properties. For example, the amino acids contribute to the
characteristic aroma, flavor, and nutritional quality of
fermented foods [28]. Furthermore, aromatic amino acids
such as phenylalanine and tryptophan, which are synthesized
via the shikimate pathway, act as the precursors for various
functional secondary metabolites synthesized via the
phenylpropanoid pathway [29, 30]. In addition, alanine,
serine, threonine, and proline are associated with sweet
taste, whereas aspartic acid, glutamic acid, and phenylalanine
contribute to an umami flavor in fermented end products
[28, 31]. On the contrary, the organic acids synthesized via
the TCA cycle, such as citric acid, malic acid, succinic acid,
and fumaric acid, have been applied commercially, such as
in the dairy, food, beverage, and pharmaceutical industries
[32, 33]. Similarly, hydrocinnamic acid acts as an inter-
mediate in phenylpropanoid synthetic pathways, which
produce coumaric acid, caffeic acid, and ferulic acid, and
exhibits various functional and pharmacological effects
[29].
Collectively, we observed that co-culturing with
fermentative inocula permutations, including CO, SA, and
SB, synergistically affected koji end products, as was
evident from their relatively higher levels of primary
metabolites than in products made using monoculture
inoculum. Previous studies suggested that the co-culture
fermentative systems offer synergistic enzyme capacity,
which potentially regulates substrate hydrolysis and
metabolite levels [25, 34]. In agreement with the results of
previous reports, we showed positive correlation between
hydrolytic enzyme activities (amylase, β-glucosidase, and
protease) and the levels of certain primary metabolite
groups in fermented rice koji.
Herein, we investigated the altered enzyme activities and
metabolite profiles in rice koji fermented using varying
inocula permutations (monoculture, co-culture, and
sequential co-culture) in a time-resolved manner. The co-
culture system showed the highest protease and β-
glucosidase activities, whereas the sequential co-culture
(SA) inocula system contained the highest concentration of
primary metabolites than monoculture or other co-culture
inocula systems. In particular, inocula permutation appears
to be an effective technical approach for designing food
fermentative systems with high levels of desired metabolites,
1980 Gil et al.
J. Microbiol. Biotechnol.
which determine the organoleptic as well as functional
aspects of the end products. However, any progress in the
development of an optimal inocula permutation will
require detailed understanding of microbial interactions
using high-throughput multi-omics and systems biology
approaches.
Acknowledgments
This work was supported by the Korea Institute of Planning
and Evaluation for Technology in Food, Agriculture and
Forestry (IPET) through the Agricultural Microbiome R&D
Program (The Strategic Initiative for Microbiomes in
Agriculture and Food), funded by the Ministry of Agriculture,
Food and Rural Affairs (MAFRA) (Grant number 918011-
04-1-HD020).
Conflict of Interest
The authors have no financial conflicts of interest to
declare.
References
1. Feron G, Bonnarme P, Durand A. 1996. Prospects for the
microbial production of food flavours. Trends Food Sci.
Technol. 7: 285-293.
2. Blandino A, Al-Aseeri ME, Pandiella SS, Cantero D, Webb
C. 2003. Cereal-based fermented foods and beverages. Food
Res. Int. 36: 527-543.
3. Jung JY, Lee SH, Jeon CO. 2014. Microbial community
dynamics during fermentation of doenjang-meju, traditional
Korean fermented soybean. Int. J. Food microbiol. 185: 112-120.
4. Kim DH, Kim SH, Kwon SW, Lee JK, Hong SB. 2013.
Fungal diversity of rice straw for meju fermentation. J.
Microbiol. Biotechnol. 23: 1654-1663.
5. Lee S, Lee S, Singh D, Oh JY, Jeon EJ, Ryu HS, et al 2017.
Comparative evaluation of microbial diversity and metabolite
profiles in doenjang, a fermented soybean paste, during the
two different industrial manufacturing processes. Food Chem.
221: 1578-1586.
6. Lee S, Lee DE, Singh D, Lee CH. 2018. Metabolomics reveal
optimal grain preprocessing (milling) toward rice Koji
fermentation. J. Agric. Food Chem. 66: 2694-2703.
7. Lee DE, Lee S, Jang ES, Shin HW, Moon BS, Lee CH. 2016.
Metabolomic profiles of Aspergillus oryzae and Bacillus
amyloliquefaciens during rice koji fermentation. Molecules 21: 773.
8. Seo HS, Lee S, Singh D, Shin HW, Cho SA, Lee CH. 2018.
Untargeted metabolite profiling for koji-fermentative bioprocess
unravels the effects of varying substrate types and microbial
inocula. Food Chem. 266: 161-169
9. Bechman A, Phillips RD, Chen J. 2012. Changes in selected
physical property and enzyme activity of rice and barley
koji during fermentation and storage. J. Food Sci. 77: 318-322.
10. Lin CH, Wei YT, Chou CC. 2006. Enhanced antioxidative
activity of soybean koji prepared with various filamentous
fungi. Food Microbiol. 23: 628-633.
11. Zhu Y, Tramper J. 2013. Koji–where East meets West in
fermentation. Biotechnol. Adv. 31: 1448-1457.
12. Leroy F, De Vuyst L. 2004. Lactic acid bacteria as functional
starter cultures for the food fermentation industry. Trends
Food Sci. Technol. 15: 67-78.
13. Kim AJ, Choi JN, Kim J, Park SB, Yeo SH, Choi JH, et al 2010.
GC-MS based metabolite profiling of rice koji fermentation
by various fungi. Biosci. Biotechnol. Biochem. 74: 2267-2272.
14. Yen GC, Chang YC, Su SW. 2003. Antioxidant activity and
active compounds of rice koji fermented with Aspergillus
candidus. Food Chem. 83: 49-54.
15. Benoit-Gelber I, Gruntjes T, Vinck A, van Veluw JG, Wösten
HA, Boeren S, et al 2017. Mixed colonies of Aspergillus niger
and Aspergillus oryzae cooperatively degrading wheat bran.
Fungal Genet. Biol. 102: 31-37.
16. Peng Y, Huang Q, Zhang RH, Zhang YZ. 2003. Purification
and characterization of a fibrinolytic enzyme produced by
Bacillus amyloliquefaciens DC-4 screened from douchi, a
traditional Chinese soybean food. Comp. Biochem. Physiol. B
Biochem. Mol. Biol. 134: 45-52.
17. Kim YS, Oh BH, Shin DH. 2008. Quality characteristics of
Kochujang prepared with different meju fermented with
Aspergillus sp. and Bacillus subtilis. Food Sci. Biotechnol. 17:
527-533.
18. Singracha P, Niamsiri N, Visessanguan W, Lertsiri S,
Assavanig A. 2017. Application of lactic acid bacteria and
yeasts as starter cultures for reduced-salt soy sauce
(moromi) fermentation. LWT-Food Sci. Technol. 78: 181-188.
19. Benoit I, van den Esker MH, Patyshakuliyeva A, Mattern DJ,
Blei F, Zhou M, et al. 2015. Bacillus subtilis attachment to
Aspergillus niger hyphae results in mutually altered
metabolism. Environ. Microbiol. 17: 2099-2113.
20. Singh D, Lee S, Lee CH. 2017. Metabolomics for empirical
delineation of the traditional Korean fermented foods and
beverages. Trends Food Sci. Technol. 61: 103-115.
21. Lee DE, Lee S, Singh D, Jang ES, Shin HW, Moon BS, et al
2017. Time-resolved comparative metabolomes for Koji
fermentation with brown-, white-, and giant embryo-rice.
Food Chem. 231: 258-266.
22. Teng D, Gao M, Yang Y, Liu B, Tian Z, Wang J. 2012, Bio-
modification of soybean meal with Bacillus subtilis or
Aspergillus oryzae. Biocatal. Agric. Biotechnol. 1: 32-38.
23. Hong KJ, Lee CH, Kim SW. 2004. Aspergillus oryzae GB-107
fermentation improves nutritional quality of food soybeans
and feed soybean meals. J. Med. Food. 7: 430-436.
24. Pérez J, Munoz-Dorado J, de la Rubia T, Martinez J. 2002.
Biodegradation and biological treatments of cellulose,
Evaluating Koji with Varying Inocula 1981
December 2018⎪Vol. 28⎪No. 12
hemicellulose and lignin: an overview. Int. Microbiol. 5: 53-63.
25. Ahamed A, Vermette P. 2008. Enhanced enzyme production
from mixed cultures of Trichoderma reesei RUT-C30 and
Aspergillus niger LMA grown as fed batch in a stirred tank
bioreactor. Biochem. Eng. J. 42: 41-46.
26. Sivaramakrishnan S, Gangadharan D, Nampoothiri KM,
Soccol CR, Pandey A. 2006. α-Amylase from microbial
sources – an overview on recent developments. Food Technol.
Biotechnol. 44: 173-184.
27. Tanyildizi MS, Ozer D, Elibol M. 2005. Optimization of α-
amylase production by Bacillus sp. using response surface
methodology. Process Biochem. 40: 2291-2296.
28. Lee GM, Suh DH, Jung ES, Lee CH. 2016. Metabolomics
provides quality characterization of commercial gochujang
(fermented pepper paste). Molecules 21: E 921.
29. Deng Y, Lu S. 2017. Biosynthesis and regulation of
phenylpropanoids in plants. CRC Crit. Rev. Plant Sci. 36:
257-290.
30. Herrmann KM. 1995. The shikimate pathway as an entry to
aromatic secondary metabolism. Plant Physiol. 107: 7-12.
31. Naumgung HJ, Park HJ, Cho IH, Choi HK, Kwon DY, Shim
SM, et al.010. Metabolite profiling of doenjang, fermented
soybean paste, during fermentation. J. Sci. Food Agric. 90:
1926-1935.
32. Karthikeyan A, Sivakumar N. 2010. Citric acid production
by Koji fermentation using banana peel as a novel substrate.
Bioresour. Technol. 101: 5552-5556.
33. Yin X, Li J, Shin HD, Du G, Liu L, Chen J. 2015. Metabolic
engineering in the biotechnological production of organic
acids in the tricarboxylic acid cycle of microorganisms:
advances and prospects. Biotechnol. Adv. 33: 830-841.
34. Tran HTM, Cheirsilp B, Hodgson B, Umsakul K. 2010.
Potential use of Bacillus subtilis in a co-culture with
Clostridum butylicum for acetone-butanol-ethanol production
from cassava starch. Biochem. Eng. J. 48: 260-267.