Exploring microbial dynamics associated with flavours production during highland barley wine fermentation

Journal Pre-proofs Exploring microbial dynamics associated with flavours production during highland barley wine fermentation Lingxi Guo, Yeming Luo, Yuan Zhou, Ciren Bianba, Hui Guo, Yemeng Zhao, Hongfei Fu PII: DOI: Reference:

S0963-9969(19)30857-9 https://doi.org/10.1016/j.foodres.2019.108971 FRIN 108971

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Food Research International

Received Date: Revised Date: Accepted Date:

26 February 2019 28 December 2019 29 December 2019

Please cite this article as: Guo, L., Luo, Y., Zhou, Y., Bianba, C., Guo, H., Zhao, Y., Fu, H., Exploring microbial dynamics associated with flavours production during highland barley wine fermentation, Food Research International (2019), doi: https://doi.org/10.1016/j.foodres.2019.108971

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Exploring microbial dynamics associated with flavours production during

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highland barley wine fermentation

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Lingxi Guo, Yeming Luo, Yuan Zhou, Ciren Bianba, Hui Guo, Yemeng Zhao, Hongfei Fu*

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College of Food Science and Engineering, Northwest A&F University, Yangling, 712100, Shannxi, P.

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R. China

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Abstract Highland barley wine (HBW) is a well-known grain wine in Qinghai-Tibet Plateau, China and is mainly fermented by local Qu (a traditional starter) with highland barley (Hordeum

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vulgare, Qingke (Tibetan hulless barley)), and the flavors profiles associated with microbiota

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succession during HBW fermentation are unrevealed. Hence, high-throughput sequencing

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(HTS) technology was used to investigate the dynamic changes of microbial community for

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the duration of the fermentation. In addition, metabolites were analyzed by gas

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chromatography-mass spectrometry (GC-MS) and high performance liquid chromatography

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(HPLC). A total of 66 volatile compounds and 7 organic acids were identified during the

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traditional brewing process. Results showed that the composition of microbiota varied over

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the fermentation process. The bacterial genera (relative abundance > 0.1%) decreased from 13

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at 0 h to 4 encompassing Leuconostoc (13.53%) and Acetobacter (74.60%) after 48 h

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fermentation, whilst the structure of fungal community was more uniform in comparison with

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bacteria, as Rhizopus and Saccharomyces were predominant throughout the fermentation.

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Furthermore, the correlations between microbiota and the detected compounds were also

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explored, which highlighted that three bacterial genera, including Acetobacter, Leuconostoc,

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Bacillus and one fungal genus Rhizopus were significantly correlated with main flavours

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compounds (|r| > 0.7, FDR < 0.01). To conclude, the detailed information provided by this

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study offer screening strategies of beneficial bacterial and fungal strains to improve the

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quality of HBW.

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Keywords: Highland barley wine; Microbial succession; Flavours changes; High throughput sequencing; Correlation analysis

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1. Introduction Highland barley wine (HBW), which is called "Qiang" in Tibetan, is one of the most

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typical representatives of Chinese traditional low-grade fermented wines (mainly distributed

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in Qinghai Province, Tibet Autonomous Region, Sichuan Province, Yunnan Province and

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Gansu Province, etc. in China) (Cao, Du, Kan, & Chen, 2012; X. Wang, Dai, Zhang, Liu, Qin,

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AO, et al., 2015). Highland barley (Hordeum vulgare, Qingke (Tibetan hulless barley)) is the

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main grain material for HBW brewing, with high nutritional value and unique flavor (Guo,

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Lin, Lu, Gong, Wang, Zhang, et al., 2018; Hu, Lin, Luo, Sun, Zhang, Wang, et al., 2016; F.

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Zhu, Du, & Xu, 2015) . HBW represents the turbid status and unique taste characteristic of

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fermented grain wine, with functional components including β-glucan and antioxidants from

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highland barley (Lin, Guo, Gong, Lu, Lu, Wang, et al., 2018).

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HBW ferments under traditional fermentation conditions using local fermentation starters,

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namely, Qu (Du, Wu, Kan, Beczner, & Chen, 2007). Cultured-based methods were applied to

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isolate cultivable strains from Qu (Du, 2008). PCR-denaturing gradient gel electrophoresis

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(DGGE) techniques were adopted to elucidate the microbial community of Qu (Zhao, 2011),

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although the PCR-DGGE technique cannot detect species present at low densities (below 103

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CFU/g or 10-100 fold less concentrated than the most abundant species of the microbial

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community) (Prakitchaiwattana, Fleet, & Heard, 2004). Thus, the microbial community

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profiles could not be clarified clearly with the mentioned methods of dependent-culture and

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independent-culture in Qu. More recently, high-throughput sequencing (HTS) technology

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were applied to investigate the microbiota of Qu samples collected from Tibet region, and the

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results demonstrated that Qu were composed mainly of Saccharomycopsis, Rhizopus, Mucor,

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Leuconostoc and Lactobacillus, etc. (Zhang, Li, Jin, Song, Zhang, Wang, et al., 2018), which

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could provide us with preliminary results of bacteria and fungi community richness and

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community diversity of original Qu, which might be similar with the microbiota of HBW at

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the start period.

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HBW fermented with single or multiple strains isolated from Tibetan Qu showed that the

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fermented product greatly differed from the traditional HBW (Du, 2008; Cao, Du, Kan, &

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Chen, 2012), suggesting that the entire Qu microbiota, not single strains or species, may be

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responsible for HBW quality and for the production of flavours and other compounds typical

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of HBW. There are few researches on the microbiota succession during the brewing process

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of traditional HBW, and the interaction between microbiota and flavour components are still

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concealed.

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In the present study, a more comprehensive analysis has been carried out to investigate the

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microbial succession and metabolites changes during HBW fermentation process. HTS

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technology was employed to exploring the dynamic changes of bacterial and fungal

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populations during the fermentation. Furthermore, the main metabolites were investigated by

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GC-MS and HPLC, correlations between the microbial dynamics and the detected compounds

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were assessed.

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2. Materials and Methods

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2.1 Chemicals

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Oxalic acid, tartaric acid, malic acid, lactic acid, acetic acid, citric acid and succinic acid

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used for quantitative analysis of HPLC and 2- Nonanone for GC-MS were purchased from

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Shanghai Aladdin Bio-Chem Technology Co., LTD (Shanghai, China).

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2.2. The traditional brewing of highland barley wine

Highland barley and Qu used in this research, mainly distributed in the southern region of

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Tibet, were purchased from a local market in Shigatse City, Tibet Autonomous Region,

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China. The traditional brewing process of HBW is shown in Fig. S1. Briefly, 30 kg highland

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barley was cooked with boiling water for 2 h to obtain soft texture. After being cooled to

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room temperature, the cooked highland barley was mixed with Qu (1000 g adding 5 g Qu),

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then transferred to wine jars wrapped in quilts for temperature retention. The fermentation

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was performed at room temperature and over a period of 48 h in solid fermentation state,

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namely Jiupei. Finally, fermented Jiupei was added with sterilized water and filtered to get a

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light yellow turbid HBW. Triplicate independent brewing was conducted.

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2.3. Sample collection

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Based on fermentation experience of winemakers, specific fermenting times were selected

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for investigation of the dynamic profiles of microbial community and flavor compounds. The

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Jiupei samples from different fermentation times (0 h, 12 h, 24 h, 36 h and 48 h, respectively)

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were aseptically collected, and transferred to sterilized tubes, thoroughly mixed, then stored at

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−80 °C before further analysis.

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2.4. DNA extraction, PCR amplification and Illumina MiSeq sequencing

The samples of different periods of HBW were collected for microbial DNA extraction

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using the Soil DNA Kit (Omega, D5625-01). Afterwards, DNA concentration were

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determined by UV spectrophotometer (Eppendorf, Bio Photometer, Germany), and its

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molecular size was estimated by 0.8 % agarose gel electrophoresis. The optimal size range to

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obtain high quality (HQ) reads is 200-450bp. The V3-V4 hypervariable regions of the

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bacterial 16S rRNA gene was amplified by polymerase chain reactions (98 °C for 5 min; 98

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°C for 10 s; 50 °C for 30 s; 25-27 cycles of 72 °C for 30 s; and a final extension at 72 °C for 5

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min) with primers F (5’- barcode + ACTCCTACGGGAGGCAGCA-3’) and R (5’-

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GGACTACHVGGGTWTCTAAT-3’) (Feng, Rong, Zhen, Yang, & Xu, 2018). For fungal

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ITS1 region was amplified by with primers F (5’- barcode +

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GGAAGTAAAAGTCGTAACAAGG-3’) and R (5’- GCTGCGTTCTTCATCGATGC-3’)

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(Buée, Reich, Murat, Morin, Nilsson, Uroz, et al., 2010). Barcode sequences are listed in

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supplementary table S1. PCR was conducted using Q5 high-fidelity DNA polymerase (NEB,

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M0491L). Amplicons were purified using the AxyPrep DNA Gel Extraction Kit (Axygen,

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AP-GX-250). The purified Amplicons were quantified on Microplate reader (BioTek,

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FLx800) with Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, P7589) and pooled

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together. DNA library preparation followed the manufacturer’s instruction (Illumina). Paired-

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end sequenced (2 × 300) on an Illumina MiSeq platform (Illumina, MS-102-3003) according

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to standard protocols by Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China).

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2.5. Processing of sequencing data

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Raw reads were processed, quality filtered and merged by FLASH software (v1.2.7,

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http://ccb.jhu.edu/software/FLASH/) (Magoč and Salzberg 2011). Subsequently, chimera

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sequences were removed using USEARCH (v5.2.236, http://www.drive5.com/usearch/) in

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QIIME software (v1.8.0, http://qiime.org/) (Caporaso, Kuczynski, Stombaugh, Bittinger, &

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Knight, 2010). Operational taxonomic units (OTUs) were clustered with a 97% similarity

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cutoff using UCLUST in QIIME software (Edgar, 2010). Based on the abundance distribution

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of OTU in different samples, alpha diversity metrics, including Chao1, ACE, Simpson and

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Simpson index were performed in QIIME.

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2.6. Determination of physicochemical properties

The physicochemical properties of the Jiupei samples, including alcohol, reducing sugar,

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pH, titratable acid, and β-glucan were assessed, respectively. The alcohol content and

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reducing sugar content were measured according to the literature (Du, Wu, Kan, Beczner, &

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Chen, 2007). A pH meter (Ohaus Corporation, Shanghai, China) was used for the

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determination of pH value, meanwhile, titration with sodium hydroxide was adopted to

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determine the titratable acid (Cao, Du, Kan, & Chen, 2012). β-glucan was measured by UV-

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VIS spectrophotometer (Shimadzu Corporation, Kyoto, Japan) using Congo red reagent

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(Zhang, Dai, Wu, Li, Chen, & Wu, 2016).

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2.7. Determination of organic acids and volatiles profiles

The organic acids from the Jiupei samples were performed by HPLC, according to

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previously described methods with some modifications (Zhou & Fu, 2013). Briefly, 5.00 g

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Jiupei sample and 5mL deionized water were placed in a centrifuge tube, ultrasonically

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assisted extraction for 10min (30 °C, and 200 w power). After that, the mixture was

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centrifuged at 10,000 rpm for 10 min at 4 °C, and the supernatant was taken out to a volume

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of 10 mL, subsequently passed through a 0.22 μm filter to be subjected to HPLC analysis. The

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separations were carried out on a Shimazu LC20A (Shimadzu Corporation, Kyoto, Japan)

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equipped with an Ecosil 120-5-C18 AQ column (250 mm × 4.6 mm i.d., 5 μm) under isocratic

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elution. Deionized water (containing 0.1% sulfuric acid, v / v) was used as the mobile phase

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with a flow rate of 0.40 mL/min. The detection wavelength was 210 nm, and the column

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temperature was set at 30 °C. Qualitative analysis was conducted with coeluted

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chromatographic standards and external standards curves were used for quantitative analysis.

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Each sample was analyzed in triplicate.

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Volatiles form the Jiupei samples were analyzed by headspace HS-SPME/GC-MS

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following the method described by Jiang et al. (2011) with modifications. Briefly, 2.00 g of

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the Jiupei sample was accurately weighed into a 15 mL amber glass vial, to which 1.00 g of

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anhydrous sodium sulfate and 10 μL of an internal standard (2-Nonanone at a concentration

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of 0.1 μg/mL) were added. The vials were tightly closed using screw-caps with silicon septum

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seal, mixed, and transferred to the tray of the auto-sampler (Shimadzu Corporation, Kyoto,

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Japan). Each sample was incubated at 60 °C for 10 min under agitation at 300 rpm, then

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volatiles were extracted using a SPME fiber coated with 50/30 μm

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divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) (Supleco, Inc.,

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Bellefonte, PA, USA) at 60 °C for 30 min. Extracted volatiles were desorbed in the injection

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port at 250 °C for 3 min, subsequently, the analytes were performed in splitless mode with

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helium as the carrier gas at 1 mL/min, on a GC-MS column (DB-1 MS, 60 m×0.25 mm i.d.,

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0.25 μm) using a Shimadzu QP2010 GC-MS (Shimadzu Corporation, Kyoto, Japan). The GC

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oven temperature was programmed from 40 °C, which was maintained for 2 min, to 120 °C at

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6 °C/min and maintained for 5 min, then ramped to 200 °C at 8 °C/min and maintained for 2

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min, after which it was ramped to 250 °C at 10 °C/min, and kept constant at 250 °C for 8 min

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before cooling back to 40 °C. The mass spectra were obtained by electron ionisation (EI) at

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70 eV with a scanning range of m/z 40–500. MS ion source and quadrupole temperatures

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were 200 °C and 150 °C, respectively. The content of each compound was calculated by

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comparison of its area with the internal standard, 2-Nonanone. Each sample was analyzed in

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triplicate.

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2.8. Data Analysis

All data were mean-centered and the variables were weighted by their standard deviation to

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give them equal variance. To assess the variation and similarity of microbial diversity over

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different samples, a principal component analysis (PCA) was carried out using SIMCS-14.1

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software (UMETRICS, Sweden) on the HTS results. The pearson’s rank correlation

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coefficients were calculated to represent beneficial or antagonistic relationships between the

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dominant microbiota by R software (v.3.5.1) with the “hmisc” package and “corrplot”

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package (Wei & Simko, 2013), and significant correlations (|r| > 0.7, FDR < 0.01) were

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shown with *.

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The organic acids and volatiles profiles were visualized with heatmap, which exhibited the

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relative abundances and hierarchical clustering of all the detected compounds at different

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fermentation times (variables clustered on the vertical axis) using R software (v.3.5.1) with

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the “pheatmap” package (Huang, Hong, Xu, Li, Guo, Pan, et al., 2018). Briefly, for

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hierarchical clustering of the heatmap, the concentrations of the detected volatiles were

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standardized using ln (concentration + 1) whilst the detected organic acids values were

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standardized using ln (concentration/10 + 1). The main differences in the detected compounds

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profiles among samples were also highlighted by PCA. Correspondence analysis (CA) of the

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detected compounds and fermentation time was also investigated by R software (v.3.5.1) with

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the “ca” package according to the literature (Nenadic and Greenacre, 2007).

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Furthermore, bidirectional orthogonal partial least squares (O2PLS) modeling based on the

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VIP(pred) method (Xiao, Xiong, Peng, Liu, Huang, Yu, et al., 2018) was performed to unveil

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the core microbiota (SIMCA-14.1 software, UMETRICS, Sweden). Briefly, microbiota data

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based on the HTS results (defined as X matrix) were mapped to the detected compounds data

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based on the GC-MS and HPLC results (defined as Y matrix), in which the VIP(pred) vector

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(VIP value for the predictive components) was calculated for each detected specie/genus.

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Subsequently, species/genera with scores of VIP (pred) > 1.0 were selected as core functional

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microbiota for the further correlation analysis. To assess the correlations between the

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microbial dynamics and the detected compounds, a matrix of Pearson's r rank correlation

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coefficients was carried out using R software (v.3.5.1) with the “hmisc” package and

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“corrplot” package (Wei & Simko, 2013), and significant correlations (|r| > 0.7, FDR < 0.01)

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were shown with *. SPSS was used for analysis of variance and significance test (SPSS 20.0,

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USA) and other mappings were displayed with Origin 8.0 (Origin Lab Corporation,

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Northampton, MA, USA).

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3. Results and discussion

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3.1. Microbial succession in HBW brewing process revealed by HTS

One of the aims of this work was to reveal microbiota diversity and community succession

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of HBW fermentation. To achieve this, HTS technology was implemented to fully

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characterize both bacteria and fungi communities. A total of fifteen Jiupei samples from five

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fermention phases were collected, then analyzed by HTS. The numbers of bacterial and fungal

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groups at each taxonomic level in the HBW Jiupei during the brewing procedure were

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obtained based on Illumina sequencing (Fig. 1 A, B), 209 genera bacterial and 155 genera

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fungal were detected at 0 h, then the number of detectable genera decreased gradually to 27

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and 84 detected at 48 h. Parameters of species richness and diversity, including chao1, ACE,

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Shannon, and Simpson index, are presented in supplementary Table S2, highly diverse

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microbial communities of bacteria and fungi were found.

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The relative abundance analysis of bacteria and fungi explained the microbiota succession

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during the HBW traditional fermentation process (Fig. 1 C, D, supplementary Table S3 and

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supplementary Table S4). The bacterial community of the Jiupei samples at the highland

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barley inoculation with Qu (0 h) compassed 13 genera or species (relative abundance > 1%):

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Pseudomonas spp. (12.83%), Ralstonia spp. (10.30%), Pelomonas spp. (9.70%),

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Ochrobactrum spp. (8.73%), Lactococcus spp. (6.73%), Acetobacter spp. (4.57%), Thermus

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spp. (3.57%), Acinetobacter spp. (3.07%), Leuconostoc mesenteroides (2.50%), Lactobacillus

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pentosus (2.17%), Bacillus subtilis (1.53%), Propionibacterium spp. (1.30%), and

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Cupriavidus spp. (1.17%). Afterwards, the relative abundance of Leuconostoc was

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dramatically enriched to 82.93% during the initial phase of fermentation (0 h - 12 h), then it

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decreased gradually as the fermentation progressed, whilst the relative abundance of

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Acetobacter increased gradually and became the most predominant bacteria genus from 36 h

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to 48 h. Lactobacillus and Bacillus were consistently detected at moderate relative abundance

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throughout the fermentation. Lactobacillus is one of the most important genera in the

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production of white wine, for promoting the fermentation, and maintaining the micro-

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ecological environment during the brewing process (Liu, Zhao, Chen, Sun, & Pan, 2018;

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Lonvaud-Funel, 1999).

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The HBW fermentation showed a less diversity of fungal population in comparison with

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bacterial communities (Fig. 1D). Rhizopus arrhizus (40.87%), Thermoascus aurantiacus

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(18.47%), Saccharomyces spp. (10.30%) and Candida spp. (9.70%) were identified as the

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predominant fungi as fermentation initialed. Among them, Rhizopus maintained higher

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proportion during the fermentation, until its relative abundance reached 83.27% at 48 h, when

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the relative abundance of Saccharomyces spp. was 4.70%. In an anaerobic environment,

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Saccharomyces spp. Strains utilize the sugars produced by Rhizopus saccharified starch to

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ferment alcohols and acids (Renouf, Claisse, & Lonvaud-Funel, 2007).

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Wine fermentations are known to harbor a heterogeneous population of microorganisms

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(Pinto, Pinho, Cardoso, Custódio, Fernandes, Sousa, et al., 2015; Portillo, & Mas, 2016). The

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composition of the HBW core microbiota at 0 h identified in this study is consistent with

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previous studies reporting the isolation of Rhizopus spp., Saccharomyces spp., and

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Lactobacillus spp. strains from Qu collected from Lhasa City (Cao, Du, Kan, & Chen, 2012;

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Du, 2008). More recently, HTS technology combined with culturing methods have been

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applied in the microbial diversity analysis in Qu purchased from a company located in Lhasa

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City (Yuan, zhang, & Xu, 2018), and the results suggested that the predominant bacteria

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genera (relative abundance) were Kocuria (6.0%), Macrococcus (4.3%), Bacillus and

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Micrococcus (3.7%), accompanied by Chryseobacterium, Acetobacter, Flavobacterium,

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Brachybacterium, Brevundimonas, Anoxybacillus, Ralstonia, and Stenotrophomonas with

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relative abundance between 1.1% to 1.5%. Rhizopus (42.0%), Aspergillus (16.4%),

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Ophiocordyceps (0.54%) and Saccharomyces (0.52%) were predominant fungal genera. It is

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remarkable that only four Saccharomyces cerevisiae, Hyphopichia burtonii, Rhizopus oryzae,

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and Alternaria alternate strains (one strain for each species) were isolated in YPD, PDA, and

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beef extract peptone media. These results suggested that most of the microorganisms in the

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Qu were difficult to be cultured, as highlighted by the detection of a larger number of taxa

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through HTS approach. Compared with the present results, the bacterial and fungal genera

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identified through HST were relatively inconsistent, suggesting differences among

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compositions of different Qu batches. A previous study on 21 HBW Qu collected from five

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different areas of the Tibet Region already showed that, besides the presence of a few shared

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dominant fungal and bacterial genera (Saccharomyces, Rhizopus and Mucor - fungal,

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Lactobacillus and Leuconostoc - bacterial), microbial populations in Qus from different and

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yet close geographical regions showed a high variability (Zhang, Li, Jin, Song, Zhang, Wang,

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et al., 2018). Compared with previous studies based on 16S/18S rDNA clone libraries and the

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PCR-DGGE technique, HTS technology revealed higher diversity, as five fungal genera of

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Bullera, Rhizomucor, Yarrowia, Simplicillim, and Peacilomyces were successfully detected

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involving in the HBW fermentation process by HTS technology for the first time, to our best

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knowledge.

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Principal component analysis (PCA) was used to assess the variation and similarity of

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microbial diversity (supplementary Fig. S2 A, B), which demonstrated the practicability and

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necessity of microbial community succession analysis, although results from this work cannot

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be generalized to every fermentation of this type considering the Qu regional differences.

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Jiupei samples exhibited varying microbiota composition as fermentation time progressed.

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Microbiota composition at 0 h was different from other fermentation times, whereas the closer

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distance between samples of 36h and 48h indicates that the composition of the microorganism

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in the late medium and final of fermentation were similar. It seems that all microorganisms

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showed different vitality and the some of the strains were stimulated to produce saccharifying

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enzyme or liquifying enzyme etc. during the fermentation, it is assumed that culture of the

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beneficial strains from the Jiupei nor the Qu samples would be reliable.

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In this work, bacterial genus Thermus and fungal genera Thermoascus and Thermomyces

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composed a small portion of the microbiota detected during the fermentation. Thermus was

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detected in Daqu (fermentation starter) of Luzhou-flavour Liquor, and it was recognized to be

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an active contributor to the aromas production of wine (Liu, Zhao, Chen, Sun, & Pan, 2018).

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3.2. Co-occurrence and Exclusion analysis reveals the relationships between different microbes

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Microbial interaction is considered an important factor that underpins the microbial

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structure (Huang, Hong, Xu, Li, Guo, Pan, et al., 2018). The Pearson’s rank correlation

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coefficients were calculated to represent beneficial or antagonistic relationships between the

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dominant microbiota (Fig. 2). The correlations between different bacteria were shown in Fig.

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2A. It was apparent that Acetobacter presented a weak negative correlation with almost other

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bacterial genera expect lactobacillus, whilst Acinetobacter, Amycolatopsis, Cupriavidus,

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Lactococcus, Methylobacterium, Ochrobactrum showed co-occurrence with Pelonmonas,

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Propionibacterium, Pseudomonas, Ralstonia, Streweptococcus and Thermus. As for fungal

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genera (Fig. 2B), Rhizopus showed strong exclusion toward Aspergillus, Bullera, Candida,

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Penicillium levitum, Rhizomucor, Simplicillium aogashimaense, Thermomyces lanuginosus,

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and Yarrowia lipolytica (P < 0.01). In addition, correlation analysis between bacteria and

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fungi indicated that Rhizopus showed exclusion with Lactococcus, Methylobacterium,

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Propionibacterium, Pseudomonas, Streptococcus, and Thermus (P < 0.01) (Fig. 2C). On the

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contrary, Aspergillus, Candida, Penicillium, Rhizomucor, Simplicillium, T. aurantiacus, T.

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lanuginosus and Yarrowia correlated positively with Acinetobactor, Amycolatopsis,

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Cupriavidus, Lactococcus, Methylobacterium, Ochrbactrum, Pelomonas, Propionibacterium,

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Pseudomonas, Ralstonia, Streptococcus, and Thermus (Fig. 2C).

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Microbial dynamics revealed by HTS technology showed that the biodiversity had a

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tendency to decrease with fermentation time for both bacterial and fungal communities, which

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implied that some microbial genera including Bacillus, Ralstonia, Pelomonas, Pseudomonas,

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Ochrobactrum, Lactococcus, Acinetobacter, Thermus, Thermoascus aurantiacus, Candida,

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and Aspergillus might be unadaptable to the accumulated ethanol concentration and

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increasing acidity etc. as a result of the selective environments, caused mainly by

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Acetobacter, Leuconostoc, and Saccharomyces. In the comparison with Lactobacillus

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predominance in Wuyi Hong Qu glutinous rice wine fermentation (Xiao, Xiong, Peng, Liu,

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Huang, Yu, et al., 2018), the predominance of Leuconostoc during whole fermentation of

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HBW contributed lower pH, and them may produce a variety of antimicrobial substances such

307

as bacteriocin to suppress the growth of numerous microbes in the brewing process, especially

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pathogens and spoilage microorganisms (Ogunbanwo, Adebayo, Ayodele, Okanlawon, &

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Edema, 2008; Okkers, Dicks, Silvester, Joubert, & Odendaal, 2010). Acetobacter and

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Rhizopus presented highly competitive abilities and became predominant during the whole

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fermentation on account of their efficient fermentation catabolism and acid tolerance (Pinto,

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Pinho, Cardoso, Custódio, Fernandes, Sousa, et al., 2015; Zhang, Jin, & Kelly, 2007).

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3.3. Changes in physicochemical characteristics during whole fermentation

The Jiupei quality attributes including alcohol, pH, titratable acid, reducing sugar, and β-

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glucan were determined, and shown in Supplementary Table S5. The alcoholic volume

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fraction of the Jiupei significantly increased (P＜0.05) during the process of HBW brewing,

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the result was in accord with literature (Du, Wu, Kan, Beczner, & Chen, 2007). The pH value

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decreased significantly (P < 0.05) from 6.06 to 4.07, with the concentration of titratable

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acidity increasing significantly (P < 0.05), from 1.64 g/kg to 4.13 g/kg during the

320

fermentation of HBW. The titratable acidity of HBW is relatively higher in comparison with

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sweet rice wine in China (Liu, 2004) and Indian rice wine (Tamang & Thapa, 2006). The

322

reducing sugar content increased significantly (P < 0.05) during the fermentation of HBW,

323

and it raised to 13.69 g/kg at the final stage of brewing, as much as half-dry yellow wine

324

according to the reducing sugar content (Wang, Mao, Meng, Li, Liu, & Feng, 2014).

325

Reducing sugar is a carbon source directly used by microorganisms i.e. Saccharomyces (Cao,

326

Du, Kan, & Chen, 2012). The concentration of reducing sugar in detected samples increased

327

continuously, according to Rhizopus abundance increase with production of saccharifying

328

enzyme and liquifying enzyme (Nie, Zheng, Xie, Zhang, Song, Xia, et al., 2017; Vegas,

329

Mateo, González, Jara, Guillamón, Poblet, et al., 2010).

330

The content of β-glucan in Jiupei samples increased gradually within 48 h of fermentation,

331

which might be related to its transport from highland barley endosperm cell wall (Lin, et al.,

332

2018). It has been reported that β-Glucan had a positive impact on variables appetite control,

333

glucose control, hypertension, and gut microbiota composition (Cloetens, Ulmius,

334

Johanssonpersson, Åkesson, & Önning, 2012), and binding properties in vitro, as well as

335

inhibitory activity on pancreatic lipase (Guo, Lin, Lu, Gong, Wang, Zhang, et al., 2018).

336

3.4. Changes in organic acids and volatile compounds during the HBW fermentation

337

Organic acids and volatiles contribute to the unique flavours of fermented products (Nie,

338

Zheng, Xie, Zhang, Song, Xia, et al., 2017). Analysis of organic acids and volatile compounds

339

in Jiupei samples from different fermentation phases were performed by HPLC

340

(supplementary Fig. S3) and SPME GC-MS (supplementary Fig. S4), respectively. The

341

assessment allowed the identification of 7 organic acids and 9 categories volatile compounds,

342

respectively (Fig. 3). This work determined the changes of organic acids, including oxalic

343

acid, tartaric acid, malic acid, lactic acid, acetic acid, citric acid, and succinic acid (Fig. 3A).

344

The content of organic acids in the Jiupei samples increased gradually and peaked at 36 h,

345

followed by a slight decrease at 48 h, when the total of 7 organic acids reached to 6190.08

346

μg/g, resulting in a relatively sour taste for HBW. Meanwhile, the proportion of these organic

347

acids varied on fermentation time. Malic acid and citric acid predominated when HBW

348

fermentation started, then the ratio of lactic acid, produced by Leuconostoc and Lactobacillus

349

etc. increased consistently until 36 h. The ratio of acetic acid, relying on Acetobacter

350

predominance, rose gradually from 36 h, which contributed to the final composition ratio was

351

as follows: oxalic acid (0.38%), malic acid (4.53%), acetic acid (8.51%), citric acid (8.90%),

352

tartaric acid (14.78%), succinic acid (18.87%), and lactic acid (44.01%) at 48 h. Organic acids

353

are one category of metabolites in the fermented grain wine. The growth of Rhizopus, and

354

Rhizomucor yielded succinic acid and oxalic acid (Millati, Edebo, & Taherzadeh, 2005). The

355

literature summarized that the five organic acids contributing to beer acidity tastes were:

356

tartaric acid, malic acid, citric acid, lactic acid and succinic acid. The harmonious sour taste

357

depended on the composition ratio and the perceivable concentration level in the substrate,

358

considering to the acidity differences, generally, tartaric acid gave tongue a feeling of bitter

359

and tough sour, and malic acid represented a bit of sharp sour, and citric acid tasted fresh and

360

cool, and lactic acid was weak in acidity, slightly with frankincense, whereas succinic acid

361

savored salty and bitter (Han, 2010).

362

Changes of composition and content of volatile substances during the fermentation of

363

HBW were illustrated in Fig. 3B, and 9 categories 66 volatile compounds were detected

364

(supplementary Table S6), including esters (25), alcohols (10), aldehydes (7), alkanes (2),

365

furans (2), hydrocarbons (2), ketones (6), pyrazines (3), and acids (9), respectively. The

366

volatile components of highland barley are mainly composed of hydrocarbon alcohols and

367

aldehydes (Hu, Lin, Luo, Sun, Zhang, Wang, et al., 2016). Volatiles complexity increased

368

with fermentation time, particularly in esters. Esters, which endue the main flavour to grain

369

wine, could be formed either via the esterification of alcohols with fatty acids or through the

370

synthesis in the microorganism's cells by alcohol acetyltransferase using acetyl-CoA and

371

higher alcohols as substrates during fermentation (Fan & Qian, 2005). Generally speaking, the

372

latter plays a more important role in the formation of esters during the fermentation (Fan &

373

Qian, 2006; Wang, Mao, Meng, Li, Liu, & Feng, 2014).

374

The organic acids and volatiles combined profiles were presented in Fig. 4, in which the

375

color intensity is proportional to the relative abundance of volatile compounds and organic

376

acids. As shown in Fig. 4, 3-methyl-1-butanol [C23], malic acid [C55], and critric acid [C38]

377

were predominant in the initial stage (0 h, and 12 h), thereafter, 3-methyl-1-butanol formate

378

[C24], 3-methylthio-1-propanol [C63], ethyl (S)-(-)-lactate [C43], (E)-9-ethyl octadecenoate

379

[C6], ethyl 9-hexadecanate [C45], ethyl acetate [C46], acetic acid [C26], amyl acetate [C31],

380

1-propanol [C11], 2-methyl-1-propanol [C19], phenylethyl alcohol [C68], hexanal [C52]

381

increased gradually, which leaded acetates and alcohols to dominate the metabolites profile at

382

48 h. In particular, 3-methyl-butanol formate [C24], tartaric acid [C70], ethyl acetate [C46],

383

2-methy-1-propanol [C19], succic acid [C69] and amyl acetate [C31] had comparatively high

384

abundances at final stage of HBW brewing (48 h).

385 386

Meanwhile, the main differences in the organic acids and volatiles profiles among samples in different brewing phases were also highlighted by PCA (Fig. 5A, B), furthermore, CA of

387

the detected compounds and fermentation time was also investigated (Fig. 5C). The PCA

388

loading plot (Fig. 5A) illustrated that the first principal component (PC1) accounted for

389

50.9% of the total variation, while PC2 explained 32.1%. Jiupei samples of different

390

fermentation phases were distributed separately (Fig. 5B). Jiupei samples from the initial

391

phase of fermentation (0h and 12h) were distributed in the first quadrant, where it was mainly

392

characterized by p-cymene [C65], methyl pyrazine [C57], 2,6-dimethyl-pyrazine [C16], and

393

2,3-butanedione [C13], 1-hexanol [C10], ethyl pyrazine [C47], 2-pentyl furan [C21], oxalic

394

acid [C64] (Fig. 5C). Jiupei samples from the medium phase of fermentation (24 h) were

395

located in the fourth quadrant and mainly characterized by 3-methy-1-butanol [C23] hexanol

396

[C52], malic acid [C55], and ethyl acetate [C46]. The CA ordination of sample variables

397

showed that jiupei samples from the final phase of fermentation (36h, and 48h) were

398

distinguished from other samples, and strongly corresponded with tartaric acid [C70], lactic

399

acid [C54], succinic acid [C69], 2-methyl-1-propanol [C19], (E)-9-octadecenoic acid ethyl

400

ester [C6], 1-propanol [C11], propanoic acid [C67], n-hexadecanoic acid [C58], acetaldehyde

401

[C25], ethyl(S)-(-)-lactate [C43], dodecanoic acid ethyl ester [C42], citric acid [C38],

402

benzaldehyde [C32], 3-methyl-butanoic acid [C4], 2,3-dihydro-furan [C14], acetoin [C28], 3-

403

methyl butanal [C3], and butanoic acid [C36], etc.

404

3.5. O2PLS-based statistical correlations between microbiota and detected compounds

405

To explore the correlation between microbes and metabolites, considering dominance and

406

functionality (Wang, Lu, Shi, & Xu, 2016), a variety of multivariate statistical analysis

407

methods have been implemented, for exmple canonical correspondence analysis (CCA), and

408

O2PLS-based correlation analysis, etc. (Nie, Zheng, Xie, Zhang, Song, Xia, et al., 2017;

409

Huang, Hong, Xu, Li, Guo, Pan, et al., 2018; Xiao, Xiong, Peng, Liu, Huang, Yu, et al.,

410

2018). In this work, O2PLS models based on the VIP(pred) method were used to reveal the

411

potential correlations between microbiota and the detected compounds during HBW

412

fermentation, by which the VIP(pred) vector (VIP value for the predictive components) was

413

calculated for detected microbiota (Fig. 6A). It was observed R2 and Q2 in the model were

414

0.958 (≈1.0) and 0.791 (> 0.5), respectively (Supplementary Table S7), suggesting the O2PLS

415

method was well fit for analysis and prediction. The VIP(pred) vector of analyzed microbiota

416

varied from 0.025 to 1.19, in which a total of 16 microbial species/genera (VIP (pred) > 1.0)

417

including 9 bacterial species/genera (VIP(pred): 1.04–1.19) and 7 fungal species/genera

418

(VIP(pred): 1.10–1.17) were recognized as influential microbiota (Fig. 6A). Thus, bacterial

419

genera including Acetobacter, Leuconostoc, Bacillus, Ralstonia, pelomonas, Ochrobactrum,

420

Acinetobacter, Thermus, Gluconobacter, and fungal genera of Rhizopus, Saccharomyce,

421

Thermoascus, Candida, Aspergillus, Thermomyces, and Bullera, were determined as core

422

functional microbiota for the correlation analysis. Subsequently, association between selected

423

microbiota and 35 compounds at higher relative abundance was shown in Fig. 6B. It has been

424

found that Acetobacter showed significant positively correlations with 17 compounds (|r| >

425

0.7, FDR < 0.01), and were strongly negatively associated with other 9 metabolites (|r| > 0.7,

426

FDR < 0.01). Notably, Acetobacter presented opposite characteristics compared with most

427

microorganisms on detected compounds. Most compounds were significantly correlated with

428

more than two core microbes, except for 2-pentyl furan [C21]. Interestingly, Ralstonia,

429

Pelomonas and Ochrobactrum were strongly associated with acids, acid precursors and

430

acetates (|r| > 0.7, FDR < 0.01), which caused the microenvironmental stress, including 3-

431

methyl butanal [C3], 3-methyl butanoic acid [C4], 1-propanol [C11], 2-methyl-1-propanol

432

[C19], ethyl 3-methyl-1-butanol formate [C24], acetaldehyde [C25], acetic acid [C26],

433

acetoin [C28], amyl acetate [C31], benzadehyde [C32], citric acid [C38], ethyl(S)-(-)-lactate

434

[C43], ethyl acetate [C46], lactic acid [C54] and n-hexadecanoic acid [C58], phenylethyl

435

alcohol [C67], succinic acid [C69], tartaric acid [C70], and tetradecanoic acid [C71]. Some

436

organic acids, such as lactic acid, and acetic acid, had adverse effects on the growth and

437

reproduction of fungi, for example, Aspergillus, Rhizomucor, Thermomyce, and Penicillium,

438

subsequently leading a relative abundance decrease (Lay, Coton, Blay, Chobert, Haertlé,

439

Choiset, et al., 2016; Ji, Jin, Yu, Mou, & Lin, 2018).

440

Acetobacter was the most dominant bacteria genus at the final stage of brewing (from 36h

441

to 48h), positively related with the relative abundance content increase of organic acids and

442

esters for example acetic acid [C26]. The correlation might be related to enzymes activities, as

443

the alcohol dehydrogenase and oxygenase synthesized by Acetobacter can promote the

444

formation of acetic acid in an aerobic environment, while the related enzymes of acetic acid

445

decomposition and transformation are relatively inhibited (Rao & Yang, 2011). Rhizopus,

446

characterized by saccharifying enzyme and liquifying enzyme (Yu & Lin, 2005), positively

447

correlated to citric acid [C38], ethyl acetate [C46], lactic acid [C54] and tartaric acid [C70], as

448

well as Leuconostoc. Leuconostoc showed the potential of producing aromatic substances by

449

glycosidase activity (Cappello, Zapparoli, Logrieco, & Bartowsky, 2016). Saccharomyces

450

exhibited insignificant relationship with the detected compounds except Butanoic acid [C70].

451

Besides, malic acid [C55] presented a significant correlation with Gluconobacter, one of the

452

main genera of acetic acid bacteria (Vegas, Mateo, González, Jara, Guillamón, Poblet, et al.,

453

2010).

454

Bacillus showed a significantly positive correlation with 3-Methyl-1-butanol [C23],

455

hexanal [C52] and malic acid [C55], and negative correlation with 3-methyl-1-butanol

456

formate [C24], amyl acetate [C31], ethyl(S)-(-)-lactate [C43], hexanoic acid ethyl ester [C53]

457

(|r| > 0.7, FDR < 0.01). It has been reported that Bacillus produced amylase and proteases

458

during the fermentative bioprocess, which was beneficial for the production of fragrant

459

substances (Chang, Moon, & Chang, 2012; Huang, Huang, & Tu, 2004; Rao & Yang, 2011;

460

Zhu, Xu, & Fan, 2010). It was reported that n-hexadecenoic acid, tetradecanoic acid, 1-

461

propanol, 3-methyl-1-butanol, 1-hexanol, 3-methyl-butyraldehyde, benzaldehyde, 2,6-

462

dimethyl-pyrazine, pyrazine, and methyl-pyrazine, were detected when African locust beans

463

were incubate B. subtilis (Azokpota, Hounhouigan, Annan, Odjo, Nago, & Jakobsen, 2010).

464

Nevertheless, further studies should be devoted to validating the association between the core

465

microorganisms and the specific flavour using multi-omics approaches (Huang, Hong, Xu, Li,

466

Guo, Pan, et al., 2018).

467

4. Conclusion

468

The microbiota diversity clearly succeeded with HBW fermentation time as observed by

469

HTS. The changes of organic acids and volatiles profiles were demonstrated at different

470

fermented time, and the interaction between the core functional microbiota and detected

471

compounds were investigated. The predominate bacteria genera Acetobacter, Leuconostoc,

472

and Bacillus, and fungi genus Rhizopus might be responsible for microorganism community

473

succession as well as associated with the taste and flavor construction of HBW. The results

474

would be useful for understanding the fermentation mechanism and quality control of

475

traditional HBW.

476

Acknowledgements

477

This work was financially supported by NWAFU alumni fund program (No. A289021601),

478

the Fundamental Research Funds for the Central Universities (No. 2452015067), and

479

NWAFU Undergraduate Training Program for Innovation and Entrepreneurship (No.

480

2201810712023).

481

482

483

Declaration of interest None.

484 485

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using an endogenous precursor approach. Journal of Industrial Microbiology & Biotechnology, 37(2),

617

17-22.

618

Zhu, F., Du, B., & Xu, B. (2015). Superfine grinding improves functional properties and antioxidant capacities of

619

bran dietary fibre from Qingke (hull-less barley) grown in Qinghai-Tibet Plateau, China. Journal of

620

Cereal Science, 65, 43-47.

621

622

Figure Captions

623

Fig. 1. Statistics of microbiota at each classification level of bacteria (A) and fungi (B), and

624

relative abundance levels of bacterial communities (C) and fungal communities (D) of Jiupei

625

samples at different fermentation times of HBW.

626

Fig. 2. Co-occurrence and co-exclusion relationships between different bacteria (A), fungi (B),

627

or bacteria and fungi (C). The figure presents a Pearson's rank correlation matrix of bacteria

628

genera or species with > 0.02% abundance, and fungi genera or species with > 0.01%

629

abundance. Strong correlations are indicated by large circles, whereas weak correlations are

630

indicated by small circles. The color of the scale bar denotes the nature of the correlation,

631

with 1 indicating a perfect positive correlation (dark blue) and −1 indicating a perfect

632

negative correlation (dark red). Only significant correlations (|r| > 0.7, FDR < 0.01) are

633

shown with *.

634

Fig. 3. Changes of 7 organic acids (A) and 9 categories volatile substances (B) of Jiupei

635

samples at different fermentation times.

636

Fig. 4. Heatmap and dendrogram of the organic acids and volatile compounds profiles

637

present in Jiupei samples at different fermentation times. The heatmap plot indicates the

638

relative abundances of volatile compounds and organic acids at different fermentation times

639

(variables clustered on the vertical axis). For hierarchical clustering of the heatmap, the

640

concentrations for the detected volatiles were standardized using ln (concentration+1)

641

whilst the detected organic acids values were standardized using ln (concentration/10+1),

642

then the color scale bar represents the normalized concentration value.

643

Fig. 5. Principal component analysis (PCA) and correspondence analysis (CA) of the relative

644

abundances of the detected compounds and fermentation time of HBW. (A) PCA loading

645

plot. (B) PCA scatter plot. (C) CA.

646

Fig. 6. Correlation analyses between microbiota and the detected compounds based on

647

O2PLS modeling during HBW fermentation. (A) VIP(pred) (variable importance for predictive

648

components) plot of the microbiota. The red, blue and green column refer to fungi (VIP(pred) >

649

1), bacteria (VIP(pred) > 1) and microbes (VIP(pred) < 1), respectively. (B) Pearson's rank

650

correlation between microbiota (VIP(pred) > 1) and the main detected compounds. The color

651

of the scale bar denotes the nature of the correlation, with 1 indicating a perfect positive

652

correlation (dark blue) and -1 indicating a perfect negative correlation (dark red). Strong

653

correlations are indicated by large circles, whereas weak correlations are indicated by small

654

circles. Only significant correlations (|r| > 0.7, FDR < 0.01) are shown with *.

655

659

660

Sample

1000

48h3

1200 48h3

48h2

48h1

36h3

36h2

36h1

24h3

24h2

24h1

12h3

12h2

12h1

0h3

0h2

0h1

The number of taxa 400

48h2

48h1

36h3

36h2

36h1

24h3

24h2

24h1

12h3

12h2

12h1

0h3

657 658

0h2

0h1

The number of taxa

656

A 600

Species Genus Family Order Class Phylum

200

0

Sample

B Species Genus Family Order Class Phylum

800

600

400

200

0

661

C

100

others Methylobacterium spp. Streptococcus spp. Amycolatopsis spp. Propionibacterium spp. Cupriavidus spp. Pantoea septica Gluconobacter spp. Thermus spp. Acinetobacter spp. Lactococcus spp. Ochrobactrum spp. Pseudomonas spp. Pelomonas spp. Ralstonia spp. Lactobacillus pentosus Bacillus subtilis Leuconostoc mesenteroides Acetobacter spp.

Relative abundance (%)

80

60

40

20

0 0h1 0h2 0h3 12h1 12h2 12h3 24h1 24h2 24h3 36h1 36h2 36h3 48h1 48h2 48h3

Sample 662 663

D

100

others Penicillium levitum Paecilomyces verrucosus Pyronemataceae spp. Simplicillium aogashimaense Yarrowia lipolytica Rhizomucor spp. Clavariaceae spp. Bullera spp. Thermomyces lanuginosus Aspergillus spp. Candida spp. Thermoascus aurantiacus Saccharomyce spp. Rhizopus arrhizus

Relative abundance (%)

80

60

40

20

0 0h1 0h2 0h3 12h1 12h2 12h3 24h1 24h2 24h3 36h1 36h2 36h3 48h1 48h2 48h3

Sample 664 665

Fig. 1.

666

667

668 669

A

B

670

671

C

672

Fig. 2.

673

A

oxalic acid malic acid tartaric acid succinic acid citric acid acetic acid lactic acid

7000 6000

Content ( μg/g )

5000 4000 3000 2000 1000 0 0

12

675

24

36

48

Time (h)

674

B

500

terpenes furans pyrazines alkanes ketones aldehydes acids alcohols esters

Content (μg/g)

400

300

200

100

0 0

12

24

Time (h) 676 677

Fig. 3.

36

48

678 679

680

Fig. 4.

681

A

682 683

684 685

B

686

C

687 688

Fig. 5.

A

B

39

Fig. 6.

40

Highlights Microbial community succession during highland barley wine brewing. Organic acids and volatiles varied on fermentation time. Correlation analysis based on O2PLS was conducted. Acetobacter, Leuconostoc, Bacillus, Rhizopus correlated with main flavours.

41

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Credit Author Statement Lingxi Guo: conceptualization, writing- original draft preparation, data curation. Yeming Luo: methodology, investigation. Yuan Zhou: methodology, investigation. Ciren Bianba: methodology, investigation. Hui Guo: data curation. Yemeng Zhao: data curation. Hongfei Fu: supervision, writing- reviewing and editing.

42