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Biotechnology and Biological Transformations

Modeling and regulation of higher alcohol production through the combined effects of the C/N ratio and microbial interaction Jian Jiang, Yuancai Liu, Huanhuan Li, Qiang Yang, Qun Wu, Shenxi Chen, Jie Tang, and Yan Xu J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.9b04545 • Publication Date (Web): 03 Sep 2019 Downloaded from pubs.acs.org on September 5, 2019

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Journal of Agricultural and Food Chemistry

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Modeling and regulation of higher alcohol production through the combined

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effects of the C/N ratio and microbial interaction

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Jian Jiang a, Yuancai Liu b, Huanhuan Li a, Qiang Yang b, Qun Wu a,*, Shenxi Chen

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b,

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a Key

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Key Laboratory of Food Science and Technology, School of Biotechnology,

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Jiangnan University, Wuxi, Jiangsu 214122, China

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b Hubei

Jie Tang b, and Yan Xu a,*. Laboratory of Industrial Biotechnology of Ministry of Education, State

Provincial Key Laboratory for Quality and Safety of Traditional Chinese

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Medicine Health Food, Jing Brand Research Institute, Jing Brand Co., Ltd,

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Daye, Hubei 435100, China

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* Corresponding author: Yan Xu, Qun Wu

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Postal address: Key Laboratory of Industrial Biotechnology of Ministry of Education,

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State Key Laboratory of Food Science and Technology, School of Biotechnology,

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Jiangnan University, Wuxi, Jiangsu 214122, China

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Tel: +86 510 85918201

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Fax: +86 510 85864112

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E-mail: [email protected] or [email protected]

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ABSTRACT

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Too large of a higher alcohol content has negative effects on the liquor taste and

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health. Revealing the key microbes and their key driving forces is essential to regulate

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the higher alcohol content in spontaneous liquor fermentation. Herein, we used high-

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throughput sequencing associated with a multivariate statistical algorithm to reveal

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the contributing microbes for higher alcohol production in Chinese light-aroma-type

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liquor and identified that Saccharomyces and Pichia were the main contributors. In

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addition, the C/N ratio and microbial interaction were found to significantly affect the

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production of higher alcohols. Herein, we used response surface methodology to

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establish a predictive model for higher alcohol production with the regulating factors,

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and the content of total higher alcohols decreased significantly from 328.80 ± 24.83

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mg/L to 114.88 ± 5.02 mg/L with the optimized levels of the regulators. This work

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would facilitate the control of flavor production via regulating microbial communities

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in food fermentation.

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KEYWORDS: higher alcohols, microbial interaction, C/N ratio, Chinese Baijiu,

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predictive modeling

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Journal of Agricultural and Food Chemistry

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INTRODUCTION

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Chinese Baijiu is one of the six famous distilled liquors in the world. The quality of

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Chinese Baijiu is critically related to the complex flavor compounds.1 Higher alcohols

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are a group of flavor compounds that coexist in fermented alcoholic beverages such as

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brandy,2 whiskey,3 Chinese Baijiu,4, 5 wine,6 Huangjiu,7 beer8. The typical higher

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alcohols in Chinese Baijiu are 2-methylpropanol and 3-methylbutanol.4, 9, 10 The

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proper higher alcohol contents contribute to the formation of the rich flavor of

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Chinese Baijiu.4, 11 However, too larger of a higher alcohol contents in liquor will

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cause a bitter taste and off-flavors as well as neurotoxic effects.12-14 It is vital to

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control the content of higher alcohols in Chinese Baijiu to improve the liquor quality.

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The higher alcohols are generally considered to be produced by Saccharomyces

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cerevisiae during the liquor fermentation process,15 and via the amino acid catabolic

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pathway (Ehrlich pathway)16, 17 along with the sugar metabolism synthetic pathway

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(Harris pathway).18, 19 Currently, methods for regulating higher alcohols in fermented

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alcoholic beverages mainly focus on genetic modification of a single strain,20-22 such

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as knockout of ILV1, LEU1, LEU2, BAT2 and IAH1 and overexpression of BAT1 and

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ATF1 in S. cerevisiae. However, these single strain modification methods are not

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applicable in spontaneous food fermentation containing multiple species. As a result,

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it would be challenging to control the higher alcohols in a multi-species food

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fermentation process.

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Chinese Baijiu is produced by a typical solid-state spontaneous fermentation process.1 The contributing microbes for the production of higher alcohols are unclear. 3 ACS Paragon Plus Environment

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In addition, the microbial community and its metabolism are strongly influenced by

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abiotic and biotic factors such as physiochemical factors and microbial interaction.23,

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the microbial community, as well as the yield of metabolites during

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biotransformation.25-27 However, the key controlling factors and their effects on

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regulating higher alcohol production in multi-species communities are still unknown.

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Thus, identifying the key higher alcohol-producing microbes and regulating their

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metabolisms based on the key controlling factors are vital to regulate the higher

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alcohols in liquor fermentation.

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For example, the C/N ratio in the substrate is an important factor which can affect

In this study, we revealed the key microbes and the key factors associated with the

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higher alcohol production. Furthermore, we established a predictive model to regulate

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the higher alcohols by using a response surface methodology with the key factors in a

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synthetic microbial community. This modeling strategy is beneficial for the control of

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flavor metabolism in food fermentation to improve the food quality.

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MATERIALS AND METHODS Samples and Analytical Reagents. Samples were collected in May 2018 from a

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famous mechanized liquor factory in Huangshi, Hubei province, China (29.76 N,

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115.42 E). A mixture of the starter (Qu), raw material (sorghum), and fermented

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grains from the last fermentation round (volume ratio = 1:100:300) was put into a

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truck-shaped tank (1.7 m × 1.0 m× 1.0 m) and then sealed for a 15-day fermentation.

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Samples (250 g) were collected from three separate fermentation tanks in the middle 4 ACS Paragon Plus Environment

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layer (0.5 m deep) on days 0, 1, 2, 3, 5, 7, 9, 11, and 14. These samples were stored at

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-20 °C for DNA extraction and physicochemical analysis. Additional samples (100g)

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from the sampling position were stored at 4 °C for strain screening.

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Ethanol (99.9%, HPLC grade) and L-menthol (99.5%) were purchased from the

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J&K Scientific Co., Ltd. (Beijing, China). Ethyl acetate, 2-methylpropanol, and 3-

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methylbutanol were obtained from the Sigma-Aldrich Co., Ltd. (Shanghai, China)

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with at least 99.0% purity.

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Yeast Strains and Culture Procedure. One S. cerevisiae strain JN-JY-1 and one

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Pichia kudriavzevii strain JN-JY-2 were used in this study. The yeast strains were

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isolated from Chinese light-aroma-type liquor fermentation and deposited in the

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China General Microbiological Culture Collection Center with the accession numbers

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CGMCC 8130 and 12418. A single colony was obtained using Wallerstein-

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laboratory-nutrient-agar (WL) medium (30 °C). The strains were activated using yeast

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extract-peptone-dextrose (YPD) medium at 30 °C, 200 rpm, for 24 h. The mono- and

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coculture were carried out using sorghum extract medium. Fermentation was

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performed at 20 °C, 200 rpm, for 72 h.

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Physicochemical Analysis. The temperature was measured in real time at the

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sampling location during fermentation. The moisture of samples was measured by

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drying 10 g of the fermented grains at 105 °C for 2 h to achieve a constant weight.

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The acidity and amino acid nitrogen content of fermented grains were analyzed by

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acid-base titration. To analyze the glucose content in the fermented grains, 10 g

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samples were mixed with 25 mL of distilled water, ultrasonically treated at 0 °C for 5 ACS Paragon Plus Environment

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30 min, and then centrifuged at 4 °C and 12857 × g for 10 min. The obtained

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supernatant was filtered through a 0.22 μm syringe filter (Nylon Acrodisc, Waters

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Co., Milford, MA). The glucose contents were determined via high-performance

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liquid chromatography (HPLC) (Agilent 1200 HPLC, Agilent Technology, Santa

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Clara, CA) and a refractive index detector (RI) (WGE, Germany). The detection

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conditions were as previously reported.28

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Determination of the Higher Alcohol Concentration. The pretreatment method

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for determining higher alcohol concentration in the fermented grains was the same as

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determining glucose content. To analyze the higher alcohol content in the fermented

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broth, 10 mL samples were centrifuged at 4 °C and 12857 × g for 10 min. The

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supernatant was then collected and stored at -20 °C prior to its analysis.

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Higher alcohols were determined by gas chromatography-flame ionization

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detection (GC-FID) as reported.29 The sampling conditions were as follows:

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supernatant (1 mL), ethyl acetate (1 mL) and L-menthol (10 μL, 10.10 g/L, internal

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standard) were mixed with 0.4 g of NaCl. After 3 minutes of oscillation, samples were

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centrifuged at 4 °C and 3214 × g for 2 min. The obtained supernatant was filtered

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through a 0.22 μm syringe filter (Nylon Acrodisc, Waters Co., Milford, MA).

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GC-FID analysis was conducted on an Agilent 7890 GC system coupled to an

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Agilent FID detector (Agilent, Folsom, CA). The supernatant (1 μL) was analyzed on

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a CP-Wax column (30 m × 0.25 mm inner diameter, 0.25 μm film thickness; J&W

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Scientific, Folsom, CA). The injector temperature was set at 250 °C, and the split

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ratio was 5:1. The oven temperature was held at 60 °C for 3 min, then raised to 6 ACS Paragon Plus Environment

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135 °C at a rate of 6 °C/min, then raised to 200 °C at a rate of 30 °C/min, and finally

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held at 200 °C for 3 min. Nitrogen was used as the column carrier gas at a constant

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flow rate of 9 mL/min. The identification of compounds was carried out by comparing

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their retention indices and higher alcohols standards.

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DNA Extraction, Amplification, and Sequencing. Total genomic DNA was

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extracted via the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA) from 7 g

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of each sample according to the manufacturer’s instructions. For fungi, the ITS2 was

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amplified by polymerase chain reactions with the primers of ITS3 and ITS4.30 For

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bacteria, the V3-V4 hypervariable region of the 16S rRNA gene was amplified by

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PCR with the universal primers of the forward 338F and the reverse 806R.31 These

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primers contained a set of barcode sequences unique to each sample. PCR products

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were purified with a PCR purification kit (TaKaRa, Dalian, China), and their

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concentrations were measured by the Thermo Scientific NanoDrop 8000 UV-Vis

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Spectrophotometer (NanoDrop Technologies, Wilmington, DE). The barcoded PCR

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products were merged into equimolar quantities and subjected to high-throughput

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sequencing using a MiSeq Benchtop Sequencer for 2 × 300 bp paired-end sequencing

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(Illumina, San Diego, CA) at Beijing Allwegene Tech, Ltd. (Beijing, China).32 All of

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the sequence data were submitted in the DNA Data Bank of Japan (DDBJ) database

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with the accession numbers of DRA008439 (16S) and DRA008440 (ITS2).

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Sequence Processing. All of the raw sequences generated from MiSeq were

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processed via the QIIME pipeline (v 1.8.0).33 Quality trimming was conducted by

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removing the sequences with quality scores < 20 or lengths < 200 bp. Sequences that 7 ACS Paragon Plus Environment

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did not perfectly match the PCR primer or had non-assigned tags were also

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removed.33 Chimeras were removed using the UCHIME software.34 Next, the

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trimmed sequences were clustered into operational taxonomic units (OTUs) at 97%

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similarity via UCLUST under the confidence threshold of 90%.35 Chao1 richness and

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Shannon diversity indices were calculated by QIIME as previously reported.33

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Real-Time Quantitative PCR (qPCR). To estimate the populations of P.

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kudriavzevii and S. cerevisiae in mono- and co-cultures, absolute quantification qPCR

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was performed on Real-Time PCR System (Applied Biosystems, Foster City, CA).

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The genomic DNA was used as the template to amplify P. kudriavzevii by primers

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PK1 and PK2, and S. cerevisiae by primers SC1 and SC2.36 Details about the primers

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were listed in Table S4. Each reaction was performed in 20.0 μL containing 10.0 μL

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SYBR Green Supermix (SYBR Premix ExTaq II, Takara, Dalian, China), 0.4 μL of

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each primer (20 μM), 1.0 μL of DNA template and 8.2 μL ddH2O. The amplification

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conditions and calibration curves were constructed as followed: preheating at 98 °C

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for 3 mins, 40 cycles of 98 °C for 30 s, 60 °C for 30 s, and an increase of 0.5 °C every

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5 s from 72 °C to 95 °C for melting curve analysis to confirm the specificity of the

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amplification.

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Box-Behnken Design. To model the effects of independent variables, including the

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C/N ratio, pH and P. kudriavzevii/S. cerevisiae ratio, a response surface methodology

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combined with Box-Behnken design was employed. The initial concentration of S.

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cerevisiae was 1 × 105 CFU/mL. The initial concentration of P. kudriavzevii ranged

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from 1 × 104 CFU/mL to 1 × 106 CFU/mL to explore the impact of the P. 8 ACS Paragon Plus Environment

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kudriavzevii/S. cerevisiae ratio. The total higher alcohol production was selected as

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the response variable. In total, 17 runs were carried out to optimize the three

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variables. The response surface (3D) and corresponding contour (2D) plots were

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generated from the models. The optimum values of the variables were calculated from

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the response surface (Design Expert software, version 8.0.6.1, Minneapolis,

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Minnesota).

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Data Analysis. Analysis of variance was conducted using one-way analysis of

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variance (ANOVA). To establish the relationship between microbial communities and

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higher alcohols, all possible Pearson correlation coefficients (ρ) among the dominant

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genera (maximum relative abundance ≥ 1%) were calculated via SPSS Statistics 22.0

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(IBM SPSS Statistics, Chicago, IL), |ρ| > 0.6 with statistically significant (P < 0.05)

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was considered as a robust correlation.37 Gephi (Web Atlas, Paris, France) was used

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to create the network to sort through and visualize the correlations.38 Redundancy

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analysis (RDA) and the Monte Carlo permutation test were conducted based on

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metabolites and the microbial communities via the vegan package in R (http://vegan.r-

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forge.r-project.org/). ANOVA was carried out to examine the statistical importance of

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regression coefficients. The coefficient of regression (R2) was calculated to find the

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goodness of fit of the model. The F–test was used to assess the significance of the

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model terms and equation.

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RESULTS AND DISCUSSION

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Dynamics of the higher alcohol contents during the liquor fermentation process. 9 ACS Paragon Plus Environment

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Figure 1 shows the dynamics of 2-methylpropanol and 3-methylbutanol in the

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fermented grains. These two higher alcohols exhibited similar dynamic profiles, and

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both reached the highest level on the 3rd day. The total higher alcohols increased

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from 51.93 ± 3.06 mg/kg to 280.34 ± 33.82 mg/kg during the liquor fermentation

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process. The accumulation of 3-methylbutanol reached 203.27 ± 23.00 mg/kg,

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accounting for 72.5% of the total higher alcohol content.

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The production rate of the two higher alcohols increased rapidly during the initial

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two days and then decreased until the end. The highest rates of 2-methylpropanol and

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3-methylbutanol production were up to 29.74 mg/(kgday) and 79.57 mg/(kgday). As

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a result, the whole fermentation process can be divided into two stages, stage I (0-2 d)

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and stage II (2-14 d), based on the production rate of higher alcohols. Higher alcohols

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mainly accumulated in stage I.

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Identification of the key higher alcohol-producing microbes during the liquor

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fermentation process. The microbial community diversity in the fermented grains

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was studied via 16S rRNA and ITS sequence analysis. Across all samples, 995,309

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reads (36,863 ± 12,221 on average) and 777,911 reads (28,812 ± 8,938 on average)

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were identified for fungi and bacteria after quality control, respectively. Meanwhile,

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we obtained 1,473 and 4,316 OTUs for fungi and bacteria, respectively, with 97%

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similarity. The Good’s coverage for ITS2 and 16S rRNA genes was over 98.8%,

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which indicated adequate sequencing depth of most of the samples (Table S1). The

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fungal and bacterial α-diversity were quantified by the Chao1 richness and Shannon 10 ACS Paragon Plus Environment

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diversity (Figure S1). A total of 51 bacterial genera and 17 fungal genera were identified in the

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fermentation process. Across all of the samples, only 17 bacterial genera and 9 fungal

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genera were dominant (maximum relative abundance ≥ 1%) (Figure 2A). For fungal

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communities, Rhizopus, Pichia and Saccharomyces were the predominant genera

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(average abundance > 10%) in stage I. In stage II, Pichia and Saccharomyces were

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still the predominant genera, while Rhizopus and Wickerhamomyces were the

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subdominant genera (1% ≤ average abundance ≤ 10%).

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For the bacterial community, Acetobacter, Gluconobacter and Lactobacillus were

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the predominant genera in stage I, while Bacillus, Geobacillus, Klebsiella,

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Leuconostoc, Pediococcus and Weissella were the subdominant genera. In stage II,

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Lactobacillus was the only predominant genus, while Acetobacter, Gluconobacter,

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Leuconostoc, Pediococcus and Weissella became the subdominant genera.

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To identify the key higher alcohol-contributing microbes, we calculated all of the

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possible Pearson correlation coefficients among the dominant genera and the contents

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of 2-methylpropanol and 3-methylbutanol. Herein, seven genera were identified that

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had significant correlations with the contents of the two higher alcohols (P < 0.05,

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|ρ| > 0.6), including five fungal and two bacterial genera (Figure 2B). Among them,

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Saccharomyces and Pichia had positive correlations with 2-methylpropanol and 3-

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methylbutanol, indicating they might be the key higher alcohol-producers.

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Identification of the key driving forces for microbial community succession 11 ACS Paragon Plus Environment

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during the liquor fermentation process. The dynamics of six physicochemical

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factors in the fermented grains during the liquor fermentation process were shown in

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Figure S2, including the temperature, moisture, acidity, glucose, amino acid nitrogen,

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and C/N ratio.

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We used redundancy analysis to uncover the major abiotic factors driving the

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microbial community succession (Figure 3). The correlations between

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physicochemical factors and microbes were calculated via variation partitioning

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analysis. Focusing on the main accumulation stage of higher alcohols (stage I), the six

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factors accounted for 89.47% of the variation in the dominant microbes (Table S2).

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The Monte Carlo permutation test indicated that the C/N ratio and acidity had

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significant influences on the dominant microbes in both stage I and stage II (Table

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S3). The factors, C/N ratio and acidity played vital roles in the succession of the

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microbial community during the liquor fermentation process, which might be related

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to the higher alcohol production.

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Effect of microbial interaction on the higher alcohol production. As mentioned

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above, we discovered that Saccharomyces and Pichia had the potential to produce

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higher alcohols, and here we evaluated the higher alcohol production capacities of

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these two potential producers based on culture-dependent methods. S. cerevisiae and

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P. kudriavzevii were the dominant species of these two genera in liquor fermentation.

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Therefore, S. cerevisiae JN-JY-1 and P. kudriavzevii JN-JY-2 were both isolated from

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Chinese light-aroma-type liquor fermentation and used in the fermentation 12 ACS Paragon Plus Environment

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experiments. The biomass of the two yeasts tended to be steady after 24 h, whereas the

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production of total higher alcohols stabilized after 60 h. After 72 h of mono-culture, S.

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cerevisiae produced a total of 370.98 ± 18.75 mg/L higher alcohols. P. kudriavzevii

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produced 38.82 ± 1.35 mg/L, accounting for approximately 10% of the production of

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S. cerevisiae. S. cerevisiae was considered to be related to the production of higher

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alcohols.17 Here we confirmed that P. kudriavzevii also had the ability to produce

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higher alcohols. This would be in line with some other studies that some non-

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conventional yeasts also have the potential to produce higher alcohols.39, 40

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Microbial interaction is an important factor that affects the quality of fermented

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foods.1 Next, we explored the impact of microbial interaction on higher alcohol

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production. We developed a S. cerevisiae-P. kudriavzevii co-culture system to study

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their interaction with respect to higher alcohol production.

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The biomass of P. kudriavzevii was nearly the same in mono- and co-cultures,

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whereas the biomass of S. cerevisiae decreased significantly in the co-cultures after

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48 h compared with that in a mono-culture. In addition, the production of higher

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alcohols decreased by 19.8% in co-cultures compared to the sum of the mono-cultures

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of S. cerevisiae and P. kudriavzevii. Co-culturing affected the specific rate of

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production of higher alcohols (Fig. 4D). These results indicated that the interaction

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between S. cerevisiae and P. kudriavzevii could regulate the microbial metabolic

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activity of higher alcohols in the artificial simulating system, which may deepen our

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understanding of the regulation of higher alcohols. 13 ACS Paragon Plus Environment

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Predictive modeling for the regulation of higher alcohols. To further analyze the

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regulation of biotic and abiotic factors on the production of higher alcohols, we

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established a predictive model for the production of higher alcohols in the co-culture

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system with S. cerevisiae and P. kudriavzevii using the C/N ratio, pH, and the

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inoculation ratio as independent variables. Based on the response surface methodology

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combined with Box-Behnken design, the empirical quadratic model was selected to

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develop the correlation between the response of the higher alcohol contents and the

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independent variables as follows:

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Y (mg/L)=148.74+25.63A-9.39B-39.57C-16.87AB-

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8.02AC+14.61BC+17.83A2+2.50B2+27.36C2.

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In the equation, Y represents the total content of higher alcohols (mg/L), A is lg Pichia

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(CFU/mL), B is the pH, and C is the C/N ratio. The F-value of the model was 13.66,

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which implied that the model was significant. The P-value of the lack of fit (P = 0.1297)

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and the model (P = 0.0012) revealed that the actual corresponding higher alcohol values

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exhibited a good fit with this model (Fig. 5A). The correlation coefficient R2 was used

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to assess the accuracy of the regression models established, with a closer value to unity

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indicating a more precise response value estimated by the models.41 In this case, the R2

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was 0.946, indicating the accuracy of the regression model.

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The effects of the tested factors on the control of higher alcohols were visualized in

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the response surfaces (Figure 6). The effects of the pH may be limited, as the P-value

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of the pH was over 0.05 (Table 2), whereas the factors lg Pichia and C/N ratio had 14 ACS Paragon Plus Environment

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significant effects on the higher alcohol production (P < 0.05). The total content of

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higher alcohols decreased to a minimal level (114.88 ± 5.02 mg/L) when the C/N ratio,

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pH and P. kudriavzevii/S. cerevisiae ratio were 271.53:1, 3.59 and 104.08:105,

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respectively. The content of total higher alcohols decreased by 65.1%, compared with

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the co-culture of P. kudriavzevii and S. cerevisiae before optimization. This indicated

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that this established predictive model would be efficient to predict and control higher

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alcohol production. Besides, the model created in this study was based on a synthetic

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microbial community including two yeasts. In the real liquor fermentation system, the

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microbial species and interactions are more complex, deeper research is needed in the

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future to explain the metabolism mechanisms of higher alcohols, which can facilitate

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the regulation the higher alcohol contents more efficient in the liquor fermentation.

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In this work, we revealed that S. cerevisiae and P. kudriavzevii were both higher

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alcohol-contributing microbes. Moreover, the C/N ratio and microbial interaction were

316

found to significantly affect the higher alcohol production. Hence, we established a

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predictive model for the production of higher alcohols based on these regulators and

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efficiently controlled the higher alcohol production. This work provides an efficient

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strategy for controlling flavor metabolism in food fermentations and is beneficial for

320

improving food quality.

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ACKNOWLEDGEMENTS

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We thank Bowen Wang and Shilei Wang for the use of R and Design Expert.

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Funding 15 ACS Paragon Plus Environment

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This work was supported by the National Key R&D Program of China

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(2018YFD0400402), the National Natural Science Foundation of China (31530055),

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the Jiangsu Province Science and Technology Project (BE2017705), the Postgraduate

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Research & Practice Innovation Program of Jiangsu Province (SJCX19_0772), the

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China Postdoctoral Science Foundation (2017M611702), and the National First-Class

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Discipline Program of Light Industry Technology and Engineering (LITE2018-12),

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and the Priority Academic Program Development of Jiangsu Higher Education

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Institutions, the 111 Project (No. 111-2-06).

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Notes

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We declare no conflicts of interest.

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ASSOCIATED CONTENT

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Supporting Information

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Chao1 indices, Shannon indices of microbial communities, dynamics of

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physicochemical factors during the liquor fermentation process, mash sample

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statistical information about Illumina sequencing results, the interpretation rate of

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single environment factor, the Monte Carlo permutation test of the dominant

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microbes, and details about the primers are showed in the Supporting Information.

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REFERENCES

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1.

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Food Sci. Technol. 2017, 63, 18-28.

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FIGURE CAPTIONS

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Figure 1. Dynamics of the two main higher alcohol contents during the liquor

483

fermentation process: (A) 2-methylpropanol and (B) 3-methylbutanol.

484

Figure 2. Dynamics of the microbial composition during the liquor fermentation

485

process. (A) Relative abundances of fungal and bacterial genera during Chinese light-

486

aroma-type liquor fermentation. (B) Correlation network between microbes and

487

higher alcohols calculated statistically (P < 0.05) by the Pearson correlation

488

coefficient (|ρ| > 0.6). Green modules represent fungal genera, and orange modules

489

represent bacterial genera. The thickness of the edges shows the value of the Pearson

490

correlation coefficient. Red and blue edges indicate negative and positive correlations

491

between the microbes and higher alcohols.

492

Figure 3. Redundancy analysis of microbes and the physicochemical factors during

493

the liquor fermentation process: stage I (A) and stage II (B).

494

Figure 4. Biomass of Pichia kudriavzevii (A) and Saccharomyces cerevisiae (B), and

495

the total production of higher alcohols in mono- and co-cultures in sorghum media

496

(C). Relationship between the specific rate of formation higher alcohols and the

497

growth rate of microbes (D).

498

Figure 5. (A) Predicted and actual values of the total higher alcohols. (B) Total higher

499

alcohol contents in the 17 runs carried out in simulated fermentations.

500

Figure 6. Response surface (3D) and corresponding contour (2D) plots for the total

501

higher alcohols. (A and B) Effects of the Pichia/Saccharomyces ratio and pH on the

502

total higher alcohol contents. (C and D) Effects of the Pichia/Saccharomyces ratio

503

and C/N ratio on the total higher alcohol contents. (E and F) Effects of the pH and

504

C/N ratio on the total higher alcohol contents.

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Table 1. Experimental design matrix for the total higher alcohols. Variables lg Pichia (CFU/mL) a pH C/N a The

Symbol coded A B C

Range and levels -1 0 4 5 3.5 4.5 50:1 237.5:1

1 6 5.5 425:1

initial concentration of Saccharomyces was 1 × 105 CFU/mL. Thus, the

Pichia/Saccharomyces ratio ranged from 1:10 to 10:1.

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Table 2. ANOVA for the response surface quadratic model. Sum of df a Squares 25561.77 9

Mean Squared 2840.20

5255.56

1

705.14 12526.23 1138.78 257.21 853.71 1338.05 26.41 3151.35 1455.18

Lack of Fit

1052.25

Pure Error Cor Total

402.92 4 27016.95 16

Source Model A-lg Pichia B-pH C-C/N AB AC BC A2 B2 C2 Residual

a df,

13.66

P-value Prob > F 0.0012

5255.56

25.28

0.0015

1 1 1 1 1 1 1 1 7

705.14 12526.23 1138.78 257.21 853.71 1338.05 26.41 3151.35 207.88

3.39 60.26 5.48 1.24 4.11 6.44 0.13 15.16

0.1081 0.0001 0.0518 0.3027 0.0823 0.0388 0.7320 0.0059

3

350.75

3.48

0.1297

F-value

Significant

Not significant

100.73

degree of freedom.

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Figure 1

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Figure 5

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Figure 6

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TOC Graphic

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