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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


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.


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).



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.


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.


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.



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.


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.

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aspergillus oryzae bacillus ) affect enzyme activities and ... to different metabolites such as...

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