Characterization of the microbial communities and their correlations with chemical profiles in assorted vegetable Sichuan pickles

Journal Pre-proof Characterization of the microbial communities and their correlations with chemical profiles in assorted vegetable Sichuan pickles Yu Rao, Yang Qian, Yufei Tao, Xiao She, Yalin Li, Xing Chen, Shuyu Guo, Wenliang Xiang, Lei Liu, Hengjun Du, Hang Xiao PII:

S0956-7135(20)30090-6

DOI:

https://doi.org/10.1016/j.foodcont.2020.107174

Reference:

JFCO 107174

To appear in:

Food Control

Received Date: 17 October 2019 Revised Date:

29 December 2019

Accepted Date: 17 February 2020

Please cite this article as: Rao Y., Qian Y., Tao Y., She X., Li Y., Chen X., Guo S., Xiang W., Liu L., Du H. & Xiao H., Characterization of the microbial communities and their correlations with chemical profiles in assorted vegetable Sichuan pickles, Food Control (2020), doi: https://doi.org/10.1016/ j.foodcont.2020.107174. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

CRediT author statement Yu Rao: Conceptualization, Methodology, Writing- Original draft preparation Yang Qian: Experiment Performance, Data Collection and Analysis Yufei Tao, Xiao She,Yalin Li, Xing Chen, Shuyu Guo, Hengjun Du: Sample preparation Wenliang Xiang, Lei Liu: Supervision Hang Xiao: Conceptualization, Supervision, Writing-Reviewing and Editing.

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Characterization of the microbial communities and their correlations

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with chemical profiles in assorted vegetable Sichuan pickles

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Yu Rao1, *, Yang Qian1, 2, Yufei Tao1, Xiao She1, Yalin Li1, Xing Chen1, Shuyu Guo1,

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Wenliang Xiang1, Lei Liu1, Hengjun Du3, Hang Xiao3, *

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1. School of Food Science and Bioengineering, Xihua University, Chengdu 610039,

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China

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2. Department of Wine and Food engineering, Sichuan Technology and Business

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College, Dujiangyan 611830, China

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3. Department of Food Science, University of Massachusetts, Amherst, Massachusetts,

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01003, USA

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* Corresponding Author:

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Yu Rao, E-mail: [email protected];

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Hang Xiao, E-mail: [email protected]

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ABSTRACT

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The Sichuan pickles of chili peppers, cowpeas and radishes were separately fermented

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for 12 days in order to identify the distinct microfloras and their correlations with the

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metabolites in different products. The chili pickle presented a slow fermentation

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process, high bacterial diversity within six phyla, and sixteen marker genera.

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Moreover, free amino acids accumulated in chili pickle and its main volatiles were

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alcohols and esters. Fast acidification and limited bacterial diversity within three

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phyla were found in Sichuan cowpea and radish pickles. The cowpea pickle was

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characterized by Lactobacillus and Pediococcus, as well as alcohols and alkenes. The

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radish pickle featured Lactococcus and Fructobacillus, as well as sulfides and

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aldehydes. Correlation analysis indicated that the metabolites, especially volatiles,

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were closely associated with not only the dominant bacteria but also those in low

28

abundance.

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Keywords: Different vegetable pickles; High-throughput sequencing; Charactersitic

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bacteria; Typical chemical compounds; Correlation analysis

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1. Introduction

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Sichuan pickle (Sichuan paocai) is a typical representative of Chinese traditional

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fermented food and its history can be traced back as far as the ancient Shang dynasty

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(Rao, et al., 2013). Similar to kimchi and sauerkraut, Sichuan pickle is a kind of

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pickles product made by lactic acid fermentation. During the fermentation, the raw

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materials for Sichuan pickle are immersed in the brine (a 6-8% salt concentration) in

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the water-sealed containers (Rao, et al., 2020). The pickle products are commonly

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served as side dishes, appetizers and condiments in Chinese cuisine (Cao, et al., 2017).

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The Sichuan pickles are produced on both domestic and commercial scales, and are

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extremely popular throughout China and even around the world (Cao, et al., 2017).

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The production yield of Sichuan pickles exceeded 5 million tons in 2018 and kept 30%

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increase per year in last five years. The huge consume demand necessitate the

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industrialization of Sichuan pickle’s production. Extensive studies have been

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conducted to provide theoretical guidance and technological support to the industrial

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production of Sichuan pickle, regarding the screening of starter cultures (Liu, et al.,

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2017), the detection of microbiota and related flavor (Xiao, et al., 2018), as well as the

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impact of chemical factors such as salt concentrations and acidity on the fermentation

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of Sichuan pickle (Cao, et al., 2017; Xiong, et al., 2016).

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However, the aforementioned studies lose the sight of peculiar feature in Sichuan

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pickle regarding the large vegetable variety. Sichuan pickle can be made of a large

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variety of vegetable species, such as cabbage, radish, chili pepper, cowpea, leaf

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mustard, bamboo shoot and celery. Researches have shown that the microbiota and

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flavor quality of pickles are dependent on diverse factors, particularly the raw

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materials (Nguyen, et al., 2013; Park, et al., 2019). In kimchi and fermented table

3

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olive, the influence of assorted raw materials on the microbial diversities have been

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confirmed (Jung, Lee, & Jeon, 2014; Kiai & Hafidi, 2014). In Sichuan pickles, some

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scattered researches recently reported different microbial features in Sichuan pickles

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with diverse vegetables. For instance, Lactobacillus, Leuconostoc, Achromobacter

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and Pediococcus dominated the fermentation of cabbage pickle (Xiao, et al., 2018),

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while Lactobacillus, Serratia, Enterobacter, Pediococcus were the main bacteria in

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Sichuan radish pickles (Yang, et al., 2018), as well as Lactobacillus, Pseudomonas,

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Vibrio and Halomonas were the leading genera in Qingcai pickle (Liang, Yin, Zhang,

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Chang, & Zhang, 2018). The microbial charecterisitics of Sichuan pickles with

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asscorted vegetables deserve further and comparative investigation. Furthermore, the

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chemical substances produced by biological metabolism during the pickle

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fermentation are complex. To our knowledge, there is limited literature regarding the

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identification of metabolite characteristics in different Sichuan pickles, as well as

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regarding correlations between the bacterial communities and chemical profiles of

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these products.

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In this study, by high-throughput sequencing and chromatographic analysis, we

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performed in-depth microbial profiling and metabolites characterization of Sichuan

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pickles of different vegetable species. Radishes, chilies and cowpeas, which are the

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most common and typical representative vegetables in Sichuan pickles, were used for

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the fermentation. Moreover, following LEfSe analysis, correlations between the

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microflora and chemical compounds in the three different Sichuan pickles were also

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examined. The aim of this study was to improve the understanding of bacterial and

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chemical diversity among different Sichuan pickles.

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2. Materials and methods

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2.1. Sichuan pickle preparation and sampling

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Fresh chili pepperes (Capsicum annuum L.), cowpeas (Vigna unguiculata (L.)

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Walp.) and red radishes (Raphanus sativus L.) were obtained from a local market in

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Chengdu, China. The vegetables were washed with tap water and dried naturally. The

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brine was prepared with cool boiled water, 6% salt (w/v) and aged Sichuan pickle

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brine (1:100, v/v). The aged brine was collected from Jixiangju Food Co., Ltd, which

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locates in the Meishan city of Sichuan province and is one of the biggest companies in

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the Chinese pickle industry. Each vegetable (1 kg) was immersed in the brine in

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individual 2.5 L glass jars. Each kind of vegetables from different batches were

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fermented in three individual pickle jars. Nine jars were all water-sealed and

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fermentation took place at room temperature (25°C - 30°C) for 12 days (Fig. S1). The

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fermentation of vegetable was conducted in triplicate. During fermentation, the brine

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was sampled every 2 days. The brine samples were centrifuged at 4°C and the

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supernatants were respectively stored at -70°C for further analysis.

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2.2. Determination of physicochemical indexes and microbial counts

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The pH values of brine samples were measured with a pH meter (PHS-3C,

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Fangzhou Technology, China). Nitrite contents were determined according to

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Özdestan and Üren (2010). The reducing sugar contents were determined by 3,5-

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dinitrosalicylic acid colorimetric method. The total sugar content was determined by

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anthrone method. The soluble protein content was determined by Coomassie Brilliant

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Blue G-250 method. The tryptic soy agar (TSA), Man-Rogosa-Sharpe agar (MRS)

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with 1% (w/v) CaCO3 and Rose Bengal agar (RB) were used for total microbial

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counts, lactic acid bacteria (LAB) counts and fungi enumeration, respectively. All the

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plates were incubated at 30°C for 48 h.

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2.3. Bacterial 16S rRNA gene amplification and Illumina sequencing

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Total genomic DNA from the brine samples was extracted using a PowerSoil

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DNA extraction kit (Mobio, US) and checked by means of a NanoDrop

106

spectrophotometer (Thermo Scientific, US). The DNA was diluted to 10 ng/µL using

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sterile ultrapure water and stored at -80°C for later use. The V4 hypervariable region

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of the 16S rRNA gene was targeted for PCR amplification with the primers 515F and

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806R (Caporaso, et al., 2011). Sequencing libraries were generated using a TruSeq

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DNA PCR-Free Sample Prep Kit (Illumina, US) and index codes were added. The

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library quality was assessed on the Qubit® 2.0 Fluorometer (ThermoFisher Scientific,

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US) and Agilent Bioanalyzer 2100 system. Lastly, the library was applied to paired-

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end sequencing (2×250 bp) with the Illumina HiSeq apparatus.

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2.4. Analysis of chemical compounds

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The organic acids (OAs), free amino acids (FAAs) and volatile organic

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compounds (VOCs) in the brine samples were analyzed. OAs were measured by high

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performance liquid chromatography (HPLC) under the following conditions: Aminex

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HPX-87H resin column (300 × 7.8 mm, Bio-Rad); operating temperature, 60°C;

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elution, 0.005 mol/L of sulfuric acid (H2SO4); flowrate, 0.6 mL/min. Eluted

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compounds were detected by a UV detector at 210 nm. Seventeen FAAs were

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analyzed by an L-8900 automatic amino acid analyzer (Hitachi, Japan).

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Analysis of the VOCs was performed by headspace solid-phase microextraction-

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gas

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DVB/CAR/PDMS fiber (2 cm length; Sigma-Aldrich, St. Louis, MO, USA) was used

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for SPME. The pickle brine (5 mL) was transferred to a 20 mL screwcap vial, after

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which each sample was supplemented with 10 µL 2-methyl-3-heptanone (200 µg/mL

chromatograph-mass

spectrometry

6

(HS-SPME/GC-MS).

A

50/30

µm

127

(w/v)) as an internal standard. A GC (GC-2010 plus, Shimadzu, Japan) fitted with a

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quadrupole MS (GCMS-QP2010, Shimadzu, Japan), using an Rtx-5MS capillary

129

column (30 m, 0.25 mm ID, 0.25 µm thickness), was used. The NIST 2011 standard

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mass spectral database was used to identify the volatiles based on the retention time

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and mass-spectral similarity match. The internal standard was used to calculate the

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relative concentrations of VOCs in different pickle groups.

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2.5. Data analysis

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The sequences were analyzed according to Usearch (http://drive5.com/uparse/)

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and QIIME (Caporaso, et al., 2010). Paired-end reads from the original DNA

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fragments were merged using FLASH (Magoč & Salzberg, 2011). Then, sequences

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were assigned to each sample according to the unique barcode. Relatively stringent

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quality controls were applied throughout. The low quality reads (with length < 200 bp,

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more than two ambiguous base ‘N’s, or an average base quality score < 30) and

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truncated sequences in which quality scores decayed (score < 11) were filtered out.

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After the discovery of duplicated sequences, all singletons were discarded as a

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potential bad amplicon (http://www.drive5.com/usearch/manual/singletons.html), thus

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resulting in an overestimation of diversity. Sequences were clustered into operational

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taxonomic units (OTUs) at a 97% identity threshold using UPARSE algorithms

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(Edgar, 2013). Representative sequences were picked and potential chimeras removed

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using the UCHIME algorithm (Edgar, Haas, Clemente, Quince, & Knight, 2011).

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Taxonomies were assigned using the SILVA database (Quast, et al., 2012) and Uclust

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classifier in QIIME.

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2.6. Multivariate statistical analysis

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Pickle fermentations were carried out in triplicate for each vegetable. All data

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were shown as the means for at least three independent experiments. p-values less

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than 0.05 were considered statistically significant. The data analysis was performed

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using R (http://www.r-project.org/) or Python (https://www.python.org/). The graph

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presentations were generated using Origin 2018 software and GraphPad Prism 7. The

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LEfSe analyses were performed using a Python LEfSe package (Segata, et al., 2011).

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The correlation index was calculated using Pearson's correlation method. Pearson's

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correlation analysis was performed using the OmicShare tools, a free online platform

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for data analysis (http://www.omicshare.com/tools). Cytoscape (3.7.1) was applied to

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visualize the interaction networks between bacteria communities and chemical

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compounds.

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3. Results

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3.1. Variations in pH, nitrite concentrations and microbial counts during different

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pickle fermentations

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In both cowpea (CP) and red radish (RD) groups, the brine pH declined sharply

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during fermentation, reaching the value of 3.5 on the 4th day (Table 1). Similarly, the

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pH in the chili (CL) brine had decreased to 4.0 on the 6th day, but subsequently

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maintained between 4.0 and 4.5. The nitrite contents rose to their peak on the 2nd day

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in all pickle jars (Table 1). In RD group, the concentration of nitrite ions reached as

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high as 20.8 mg/kg on the 2nd day, but decreased at least one order of magnitude from

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the 4th day onwards.

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Despite being introduced into the same microbial communities (the aged brine),

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the total microbial counts, LAB and fungi in the three groups of pickle fermentation

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presented certain differences in their dynamic changes during the fermentation

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process (Fig. 1). In the CP and RD fermentation, total microbial count reached a peak

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on the 4th and 6th day respectively, and were both dominated by LAB. The microbial

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growth in the CL fermentation was comparatively slow, with total microbial count

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still below 7.0 log cfu/mL on the 6th day, reaching a peak by the 8th day. By the late

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fermentation period, the total microbial count and LAB counts in all three groups of

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pickle brine had declined altogether. Although the fungi count continued to increase

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in all pickles throughout the fermentation processes, they remained consistently below

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6.0 log cfu/mL. Based on the above results, the 6th day was the mid- or turning point

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of microbial and physicochemical changes in the three kinds of vegetable pickles. The

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fermentation processes of all pickle groups were divided into two stages, namely

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stage I (from day 0 to day 6) and stage II (from day 7 to day 12).

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3.2. Bacterial communities at the Phylum and Genus levels during different pickle

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fermentations

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The bacterial communities in day 6 and day 12 were further investigated. A total

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of 203,133 resampled sequencing reads were generated from the 21 Sichuan pickle

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samples with different vegetable species. Among these reads, 3,482 unique and

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classifiable representatives were identified at a high sequence similarity level of 97%.

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As shown in Fig. S2, the Shannon curves reached saturation phase, indicating that

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most bacterial phylotypes present in the brine had already been captured.

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The relative abundance at the phyla and genera levels in different vegetable

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Sichuan pickles were analyzed. The phyla, whose abundance made up at least 0.1% in

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each pickle group, are shown in Fig. 2. Three phyla were found in CP and RD pickles,

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while six phyla were found in CL pickles. Firmicutes presented the highest relative

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abundant in all samples, followed by Proteobacteria and Bacteroidetes. The relative

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abundance of Firmicutes in the aged brine, as well as the CP and RD pickles, was 9

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found to be in excess of 90%, while in the CL pickle the relative abundance of

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Firmicutes was only 65.6% on the 6th day, increasing to 86.3% on the 12th day.

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Compared with aged brine, the relative abundance of Proteobacteria in the CL and RD

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pickles increased during the fermentation, reaching 30.7% in the CL sample on the 6th

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day.

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The top 10 genera in each pickle group are also identified in Fig. 2. Lactobacillus

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was found to have the highest relative abundance in aged brine and all vegetable

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pickles. Compared with the aged brine, the relative abundance of Lactobacillus genus

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in the CL pickle decreased to 58.8% on the 6th day but had recovered slightly to 69.3%

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by the 12th day. Unclassified Enterobacteriaceae, Pediococcus, Enterobacter and

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Lactococcus accumulated during the CL pickle fermentation, with their relative

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abundances reaching more than 1.0%. The relative abundance of Lactobacillus genus

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in 6 days’ CP pickle samples maintained 91.4%, but decreased in the followed 12th

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day to 77.9%. Pediococcus and unclassified Enterobacteriaceae ranked as the second

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and third highest relative abundances, respectively, in the CP pickle samples. The

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relative abundance of the Lactobacillus genus in the RD pickle samples was recorded

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as 82.7% on the 6th day and 89.5% on the 12th day, and RD’s relative abundances of

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unclassified Enterobacteriaceae and Lactococcus were higher than those in the aged

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brine.

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3.3. Microbial diversity and features in different pickle samples

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The microbial ɑ-diversity indices, richness indices (Chao 1) and diversity indices

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(Shannon) were evaluated (Fig. 3A, 3B). The results indicated that the bacterial

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diversity of the CL pickle on day 6 was significantly different from those of the aged

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brine and other pickle samples. Moreover, the weighted UniFrac distances-based

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principal coordinates analysis (PCoA) showed that the bacterial compositions during 10

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the fermentation of different pickles were obviously different from that of the aged

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brine (Fig. 3C). A distinct clustering of the microbiota communities also existed

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among the CL, CP and RD pickles (Fig. 3D).

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In addition, LEfSe was performed to obtain the greatest differences in taxa within

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the aged brine and different pickles (Fig. 4A). A total of 70 taxa were found to

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represent a remarkable difference in their relative abundance, with an LDA score log

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of 10 > 2. Their cladogram representation and the predominant bacteria of the

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microbiota are shown in Fig. 4B.

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The results showed that fourteen genera and one species belonging to the

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Proteobacteria, namely Pectobacterium, Aeromonas, Citrobacter, Sphingomonas,

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Bilophila,

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Acinetobacter, Proteus, Paenalcaligenes, Vibrio and Enterobacter hormaechei, were

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the marker bacteria during the fermentation of CL pickle (especially on the 6th day),

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as well as Lachnospiraceae NK 4A136 group, belonging to the Firmicutes, and

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Rikenellaceae RC9 gut group, belonging to the Bacteroidetes. Lactobacillus and

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Pediococcus (especially Ped. ethanolidurans and Lb. fermentum), belonging to

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Firmicutes, were found to be the characteristic bacteria of CP pickle on the 6th and the

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12th day, respectively. During the RD pickle fermentation, Lactococcus and Lb.

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plantarum were clearly distinguishable on the 6th day, while Fructobacillus and Leu.

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mesenteroides were identified as the marker bacteria on the 12th day.

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3.4. Changes in concentrations of OAs and FAAs during different pickle

245

fermentations

Halomonas,

Burkholderia,

Cronobacter,

Pantoea,

Pseudomonas,

246

As shown in Table 2, the concentrations of lactic acid were found to increase

247

during fermentation in all groups. The CP pickle presented a significantly higher level

11

248

of lactic acid than the CL and RD pickles on both the 6th and 12th days, while the

249

content of acetic acid in the CL pickle was similar to that of the CP pickle in each of

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the fermentation stages. Compared with the CL and CP pickles, the RD pickle

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contained more acetic acid but, exceptionally, this concentration declined in the later

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stage of fermentation.

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Seventeen FAAs were detected in all pickle samples at different stages of

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fermentation (Fig. 5). In the CL pickle, each FAA, as well as the total FAA content,

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increased obviously during fermentation. The levels of umami-tasting FAAs, glutamic

256

acid (Glu) and aspartic acid (Asp), were doubled on the 12th day. The concentrations

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of the sweet-tasting FAAs, alanine (Ala), proline (Pro) and glycine (Gly), were

258

separately 4, 6 and 11 times higher than those in the fresh chili. In the CP

259

fermentation, there was no significant difference in the total content of FAAs between

260

the 6th day’s pickle sample and that on the 12th day. In the RD fermentation, most

261

FAAs presented with parabolic changes, with the total content increasing after 6 days’

262

fermentation but subsequently decreasing even lower than the FAA level of the fresh

263

radishes.

264

3.5. Profiles of VOCs during different pickle fermentations

265

In the CL brine sample, 91 and 118 kinds of VOCs were found on the 6th and 12th

266

days, respectively; in the CP brine sample, 82 and 101 kinds of VOCs were found on

267

the 6th and 12th days, respectively; and, in the RD brine sample, 99 and 99 kinds of

268

VOCs were found on the 6th and 12th days, respectively (Fig. S3). All VOCs were

269

attributed to 11 classes, including acids, alcohols, aldehydes, alkanes, arenes, esters,

270

ethers, ketones and sulfides, amongst others. The top thirtieth dominant VOCs in each

271

pickle brine, ranked according to their concentrations, are listed in Table S1. The

12

272

results of the PCA conducted on the basis of the relative abundance of each VOC in

273

the different pickle brines are shown in Fig. 6.

274

As shown in Table S1 and Fig. S3A, alcohols and esters were enriched most

275

during the CL pickle’s fermentation. Linalool and cineole was detected on both the 6th

276

and 12th days, while 2-hexyl-1-decanol, 4-methyl-1-pentanol and 2-methyl-1-octanol

277

were only detected on the 12th day. Methyl salicylate, 4-tert-butylcyclohexyl acetate,

278

isobornyl acetate and 2,4-di-tert-butylphenol were the dominant ester compounds in

279

the CL pickle. Combined with the PCA analysis in Fig. 6, linalool, 4-methyl-1-

280

pentanol, cineole and methyl salicylate were the dominant and discriminant VOCs in

281

the CL pickle. High levels of alcohols and alkanes were noted in the CP pickle brine

282

samples (Table S1 and Fig. S3B). Alcohols, mainly 3-octanol and 3-octenol, had

283

increased significantly by the 6th day and their levels maintained until the 12th day.

284

PCA showed that the dominant 3-octanol and 3-octenol were the representative VOCs

285

in the CP pickle (Fig. 6). Dimethylhexene and cycloheptanemethanol were found to

286

be the dominant alkenes and alkanes, which enriched during the fermentation of CP

287

pickle. The VOCs in the RD pickle were dominated and characterized by sulfides,

288

especially piperidine-2-thione, dimethyl trisulfide and 3-(methylthio) propyl

289

isothiocyanate (Table S1 and Fig. S3C). High levels of aldehydes and alcohols were

290

also detected in the RD pickle brine. Dimethyl benzaldehyde and nonanal, also

291

detected in the CL and CP pickle brine, were the main aldehydes in the RD pickle

292

brine. 1-dodecanol was also the discriminant VOCs in the RD pickle brine, further

293

enriched during fermentation (Fig. 6).

294

3.6. Correlation between bacterial communities and chemical compounds

13

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The Pearson rank correlations between dominant genera (relative abundance > 0.1%

296

and the marker bacteria) and the chemical compounds (VOCs, FAAs and organic

297

acids) in the different pickle samples are shown in Fig. 7 and Table S2. Thirty-five

298

genera and two species in the CL pickle were used to analyze the correlations with

299

chemical compounds, most of which were found to correlate with alcohols, as shown

300

in Fig. 7A. Pediococcus was positively correlated with linalool (ρ = 0.89) and 4-

301

methyl-1-pentanol (ρ = 0.88), while Aeromonas was negatively correlated with

302

linalool

303

Enterobacteriaceae (ρ = - 0.82), Allobaculum (ρ = 0.92), Cronobacter (ρ = - 0.83) and

304

Vibrio (ρ = - 0.91). In all, 24 genera and two species were found to be correlated with

305

esters in the CL pickle, and 27 genera and two species were found to be correlated

306

with 17 FAAs. Among those genera correlating with FAAs in the CL pickle,

307

Pediococcus, Enterobacter and Burkholderia correlated significantly with more than

308

10 FAAs. Lactobacillus and Pediococcus were positively correlated with lactic acid

309

(ρ = 0.87), while 17 genera and two species were negatively correlated with lactic

310

acid. Pectobacterium was positively correlated with acetic acid (ρ = - 0.84). Nitrite

311

was correlated with Pediococcus (ρ = - 0.86) and Aeromonas (ρ = 0.83).

(ρ

=

-

0.83).

Methyl-1-pentanol

correlated

with

unclassified

312

In the CP pickle, twelve genera and two species were analyzed, among which

313

eight genera and two species were correlated with alcohols, as shown in Fig. 7B.

314

Lachnospiraceae NK4A136 group and Exiguobacterium were correlated with the

315

marker alcohol cycloheptanemethanol (ρ > 0.84), while unclassified Streptococcaceae

316

and Sporosarcina were correlated with alkenes (ρ = - 0.81 and ρ = 0.88, respectively)

317

in CP pickle. Five genera, namely Lactobacillus, Pediococcus, unclassified

318

Enterobacteriaceae, Lactococcus and Enterobacter, as well as one species, Ped.

14

319

ethanolidurans, were correlated with FAAs. Unclassified Enterobacteriaceae was

320

correlated with acetic acid in the CP pickle (ρ = 0.85).

321

Eighteen genera and two species were analyzed in the RD pickle, as shown in Fig.

322

7C. Lactococcus and unclassified Streptococcaceae were positively correlated with 3-

323

(methylthio) propyl isothiocyanate (ρ > 0.89), and Lactococcus and Pediococcus were

324

correlated with piperidine-2-thione (ρ = 0.93 and ρ = - 0.87, respectively).

325

Lactobacillus, Lactococcus, unclassified Streptococcaceae and Lb. plantarum were

326

correlated with seventeen FAAs. Lactococcus and unclassified Lactobacillales were

327

negatively correlated with both lactic acid and acetic acid, while Pediococcus and

328

Allobaculum were positively correlated with acetic acid. Lactobacillus, especially Lb.

329

plantarum, was negatively correlated with nitrite (|ρ| ≥ 0.83), however Lactococcus

330

was positively correlated with nitrite (ρ = 0.84).

331

4. Discussion

332

In different Sichuan pickles, the most abundant phyla were found to be Firmicutes,

333

Proteobacteria and Bacteroidetes (Fig. 2), while the most abundant genera in all

334

groups was Lactobacillus, which has well-established roles in the fermentation of

335

Sichuan pickles (Liu, et al., 2019; Yang, et al., 2018). Pediococcus, Lactococcus,

336

Enterobacter and unclassified Enterobacteriaceae, which are known to be common

337

bacteria species in fermented vegetables (Di Cagno, Filannino, & Gobbetti, 2016),

338

were measured with different levels of relative abundance in the CL, CP and RD

339

pickles. Despite the same manufacturing processes, environmental conditions and the

340

same inoculation with aged brine, significant variances were found in the phyla and

341

genera present in low abundance in the Sichuan pickles of different vegetable species

342

(Fig. 2 and Fig. 3). These findings, therefore, suggest that the microbial variations in

343

the study’s Sichuan pickles originated on the vegetable surfaces (Di Cagno, Coda, De 15

344

Angelis, & Gobbetti, 2013) and that the formation of distinctive microbial community

345

structures in each Sichuan pickle was dependent on the vegetables’ distinct species,

346

rather than on the environmental or manufacturing conditions.

347

The CL fermentation process was tardy, featuring a slow LAB growth, a low

348

content of organic acid and a gentle pH decline (Table 1 and Fig. 1), which was due to

349

the low level of quick-acting carbon sources. The contents of total and reducing sugar

350

in the chili juice were significantly lower than those of the radish and cowpea (Table

351

S3). The Pearson rank correlation analysis showed that Lactobacillus and

352

Pediococcus in the CL pickle were significantly positively correlated with lactic acid,

353

which concurs with the results of Liu, et al. (2019). Another 20 genera or species were

354

negatively correlated with lactic acid and acetic acid, suggesting that, while only

355

slightly abundant, these bacteria may be important for the changes in OAs during the

356

CL fermentation (Figure 7). An alleviated pH condition made the CL pickle relatively

357

higher in Proteobacteria abundance, which is consistent with the results reported by

358

Cao, et al. (2017). The survival pressure on the microorganisms in the CL pickle was

359

found to be significantly lower than on those in the CP and RD pickles, as its higher

360

pH condition enabled more species to survive as a symbiotic community and resulted

361

in higher bacterial diversity. The slightly abundant phyla Actinobacteria,

362

Verrucomicrobia and Acidobacteria, the common rhizosphere bacteria of vegetable

363

plants (Mendes, Garbeva, & Raaijmakers, 2013), were only found to exist in the CL

364

pickle (Fig. 2). Sixteen genera were unique marker bacteria in the CL pickle (Fig. 4)

365

and these were all of a low relative abundance, with most below 1.0% except for

366

Pectobacterium with 1.0%~1.8% relative abundance.

367

Compared with the CL pickle, both the CP and RD pickles showed a sharper pH

368

decline and lower bacterial diversity, especially during the initial stage of 16

369

fermentation (0-6 days). Cowpeas and radishes have relative high total and reduced

370

sugar contents (Table S3), which attributed to the rapid growth of LAB and obvious

371

accumulation of OAs in the pickles (Filannino, et al., 2014). The striking pH decrease

372

in the CP pickle to 4.5 on the 2nd day, indicated that it had the most acidic

373

environment, accounting for its simplest microbial diversity (Fig. 3). As the

374

distinguishing genera, Lactobacillus and Pediococcus together occupied a relative

375

abundance of more than 96.3% in the CP pickle (Fig. 2), which might explain the

376

enrichment of lactic acid and the rapid pH decline. In the RD pickle, the characteristic

377

bacteria were Lactococcus (4.7%~1.6%), Fructobacillus (0.2%~0.3%), Lb. plantarum

378

(4.1%~4.9%) and Leu. mesenteroides (0.2%~0.3%). Leu. mesenteroides and Lb.

379

plantarum are respectively considered to be the most important bacteria during

380

vegetable fermentation and are always screened as starter cultures (Jung, et al., 2014;

381

Pérez-Díaz, et al., 2017). The accumulated acetic acid in the RD pickle (Table 1),

382

which might have contributed significantly to its aroma, was positively correlated

383

with Pediococcus (1.9%~2.5%) and Allobaculum (0.1%~0.2%), indicating that it was

384

mainly produced by the two genera.

385

FAAs are present in different combinations in fermented products and are the

386

main contributors to their delicate flavours (Charve, Manganiello, & Glabasnia, 2018).

387

The fresh chilies in the present study were found to have a lower FAA content than

388

the fresh radishes and cowpeas (Fig. 5), however, the content of FAAs in the CL

389

pickle increased continuously during fermentation and had reached 1.27 g/L by the

390

12th day. The contents of Glu (61.0 mg/L) and Asp (99.0 mg/L) were much lower than

391

the taste threshold values under the low pH conditions (pH<4.3) and imparted umami

392

tastes to the CL pickle (Schoenberger, Krottenthaler, & Back, 2002). The sweeter

393

tasting FAAs in the CL pickle, such as Gly, Ala, Ser, Pro, Asn and Thr, were

17

394

obviously higher than in either the CP or RD pickles (Fig. 5). The richly flavored

395

FAAs in CL pickle may be one of the pivotal reasons why it is one of the most

396

popular condiments in Chinese cuisine. It is widely accepted that FAAs are generated

397

through primary proteolysis of raw materials by protease from LAB during food

398

fermentation (Zhao, Schieber, & Gänzle, 2016). Pediococcus, Enterobacter and

399

Burkholderia, which were found to be remarkably positively related to more than 10

400

FAAs in the CL pickles (Fig. 7), might be important in the proteolysis and

401

metabolism of amino acids. The FAA content in the RD pickle declined after the 6th

402

day (Fig. 5), suggesting that long-term fermentation was of no benefit to its flavor. Lb.

403

plantarum, which was found to be one of the marker bacteria during the stage II of

404

RD fermentation, seemed to be mainly correlated to the FAA degradation (|ρ| > 0.80,

405

Fig. 7).

406

The production of aroma compounds, which tend to be an important development

407

of flavor specific to the pickle products, depends on the microbial fermentation, as

408

well as the presence of precursors in the vegetables. It is worth noting that the VOCs

409

of different Sichuan pickles in the present study were not only associated with the

410

high relatively abundant genera but were also noticeably correlated with the low

411

relative abundance of marker genera. Alcohols and esters were found to be the most

412

abundant VOCs in the CL pickle, similar to the results reported by Z. B. Xiao, et al.

413

(2010). Some VOCs were already present in the fresh chili, such as linalool and

414

methyl salicylate (Data not shown), and the fermentation process promoted the

415

liberation and conservation of these compounds (Li, Jeon, Kwon, Huang, & Baek,

416

2019). Nearly all the bacteria in the CL pickle were associated with the VOCs, among

417

which the slightly relative abundant genera Arthrobacter (0.1%~0.3%) and Bilophila

418

(0.1%) dramatically correlated with more than thirty VOCs, while Acinetobacter

18

419

(0.1%) displayed correlation with 22 VOCs (Fig. 7). Nineteen genera showed

420

correlations with more than 10 VOCs, most of which were of low abundance, below

421

1.0%. These results suggest the pivotal significance of low abundance bacteria in the

422

formation of the unique flavor of CL pickle. Although CP pickle displayed low

423

bacterial diversity, its VOC contents also mainly correlated with low abundant genera,

424

namely the uncultured Bacteroidales S24-7 group, Lachnospiraceae NK4A136 group,

425

unclassified Streptococcaceae and Exiguobacterium. These genera prominently

426

correlated with more than twenty VOCs in the CP pickle. Moreover, the lactic acid

427

fermentation may also prevent the flavor formation. In present study, volatile

428

aldehydes, which were the major compounds in fresh chili and cowpea juices, were

429

scarce in the CL and CP pickles (Fig. S3). The pH drops in CL and CP pickles after

430

the fermentation might inactivate the enzymes which formed these compounds when

431

the vegetable tissues were disrupted (McFeeters, 2004).

432

In the RD pickle, the volatile sulfide compounds, as well as aldehydes, were

433

identified as the discriminant VOCs, which is consistent with previous radish pickle

434

findings (Zhao, et al., 2016). These volatile sulfide compounds, including piperdine-

435

2-thione, dimethyl trisulfide and 3-(methylthio) propyl isothiocyanate, have highly

436

distinctive olfactory properties due to their super-low odor thresholds and have been

437

described as sulfurous and spicy (Pogačić, et al., 2016). It is known that sulfide

438

compounds are generated mainly through the enzymatic breakdown of precursor

439

glucosinolates when the cellular structure of the radish is disrupted and fermented

440

(Chen, et al., 2016; Chen, et al., 2017). Here, the Lactococcus and unclassified

441

Lactobacillales were found to be positively correlated with the generation of sulfide

442

compounds in the RD pickle. Dimethyl benzaldehyde, described as having an almond

443

odor, and nonanal, with a melon peel odor, are also noted in fresh radish (Data not

19

444

shown) (Chen, et al., 2017; Zhao, et al., 2016). Notably, the slightly abundant genera

445

Weissella (0.3%) and Allobaculum (0.1%~0.2%) were correlated with 18 and 19

446

VOCs, respectively, in the RD pickle.

447

In addition, the potential food safety issues caused by this product’s nitrite content

448

deserve attention. During vegetable fermentation, nitrates existing in the plant tissue

449

can be reduced to nitrites (Vázquez-Torres & Bäumler, 2016). Compared with chilies

450

and cowpeas, radishes contain a significantly higher nitrate concentration (Alexander,

451

et al., 2008), which is why the RD pickle in this study presented a higher nitrite level.

452

The nitrite contents peaked on the 2nd day in all pickles (Table 1), which is consistent

453

with the reports of Yan, Xue, Tan, Zhang, and Chang (2008). The acceptable daily

454

intake of nitrite recommended by the World Health Organization is 0.07 mg/kg bw

455

(body weight) (WHO, 2002), suggesting that eating approximately 200 g RD pickle

456

(the 2nd day) at one time would lead to a health risk in a 60 kg adult. Fortunately,

457

however, the nitrite concentration decreased by at least one order of magnitude from

458

the 4th day in this study’s RD group. The reduction of nitrite in the RD pickle was

459

remarkable correlated with Lactobacillus, including Lb. plantarum, which is in

460

agreement with previous studies (Wang & Shao, 2018). However, Lactococcus in the

461

RD pickle was positively correlated with its content of nitrite.

462

5. Conclusion

463

The study presented a detailed analysis and extensive comparison of the bacterial

464

diversities and chemical profiles in different Sichuan pickles of separately fermented

465

chilies, cowpeas and radishes. In addition, the influence of vegetable species on the

466

microbial structure and dynamics during the fermentation has been demonstrated, and

467

the marker bacteria and characteristic metabolites in Sichuan pickles have been

468

ascertained. The cowpea and radish pickles presented the relative fast fermentation 20

469

process and limited bacterial diversity. Lactobacillus and Pediococcus were the

470

typical genera in cowpea pickle, which was characterized by alcohols and alkenes.

471

Lactococcus and Fructobacillus were the marker genera in radish pickle, which was

472

featured by sulfides and aldehydes. Slow acidification and sixteen maker genera were

473

found in chili pickle and the dominant volatiles in chili pickle are alcohols and esters.

474

It was also interesting that the low abundant bacteria were found to be significantly

475

correlated with the metabolites, especially volatiles, in Sichuan pickles. This work

476

facilitates further understanding of the correlations between bacterial communities

477

and chemical profiles, and emphasizes the importance of low-abundance bacterial

478

genera on the organoleptic attributes (especially VOCs) in Sichuan pickles.

479

6. Conflict of interest

480

481

The authors declare that there are no conflicts of interest relevant to this study. 7. Fundings

482

This work was financially supported by the National Science Foundation for

483

Young Scientists of China (31701579, to Yu Rao), the Applied Basic Research

484

Programs of Science and Technology Department of Sichuan Province (2019YJ0388,

485

to Yu Rao), the Science Program of Sichuan Provincial Department of Education

486

(17ZB0413, to Yu Rao), and the Young Scholars Reserve Talents Program of Xihua

487

University (0220170307, to Yu Rao).

21

488

8. References

489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538

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25

656

Table 1 The pH values and nitrite (ion) concentrations in different Sichuan pickles

657

during fermentation. Nitrite ion concentration (mg·kg-1)

pH value Day

658

a

CLa

CP

RD

CL

CP

RD

0

6.5±0.0

6.5±0.0

6.5±0.0

0.4±0.0

0.4±0.0

0.4±0.0

2

5.8±0.2

4.5±0.0

5.3±0.2

2.6±0.3

6.2±1.0

20.8±2.2

4

4.8±0.2

3.5±0.0

3.5±0.0

1.5±0.2

0.3±0.1

1.1±0.3

6

4.2±0.2

3.5±0.0

3.5±0.0

1.9±0.1

0.7±0.2

2.0±0.3

8

4.5±0.0

3.5±0.0

3.5±0.0

0.1±0.0

0.2±0.1

1.3±0.2

10

4.3±0.2

3.5±0.0

3.5±0.0

0.1±0.0

0.0±0.0

2.0±0.4

12

4.0±0.0

3.5±0.0

3.5±0.0

0.5±0.1

0.0±0.0

1.2±0.2

CL, chili pickle; RD, red radish pickle; CP, cowpea pickle.

26

659

Table 2 The concentrations of organic acids in different Sichuan pickles during the

660

fermentation.

Concentration (mM)

661

CLa

CP

RD

6th day

12th day

6th day

12th day

6th day

12th day

Lactic acid

5.00±1.22c

7.78±0.75c

27.80±12.14b

51.29±6.68a

13.76±3.39c

15.85±1.40bc

Acetic acid

13.88±0.40b

14.22±0.10b

14.50±0.75b

16.16±0.54b

23.22±5.66a

16.42±0.37b

a

CL, chili pickle; RD, red radish pickle; CP, cowpea pickle.

27

662

Fig. 1. The microbial growth in the (A) chili pickle, (B) cowpea pickle and (C) radish

663

pickle brines during the Sichuan pickle fermentation. CL, chili pickle; RD, red radish

664

pickle; CP, cowpea pickle.

665

28

666

Fig. 2. Comparison of bacterial compositions in the (A) aged brine, (B) chili pickle,

667

(C) cowpea pickle and (D) radish pickle groups at phylum and genus levels. Different

668

colors indicate different fermentation times, with pink and dark red indicating the 6th

669

and 12th day of fermentation, respectively. AB, aged brine; CL, chili pickle; RD, red

670

radish pickle; CP, cowpea pickle.

671

29

672

Fig. 3. Comparison of ɑ and β diversity indices of bacterial communities across

673

different groups of Sichuan pickles: (A) Chao1; (B) Shannon index; (C) UniFrac

674

weighted principal coordinate analysis (PCoA) including aged brine sample; and (D)

675

UniFrac weighted principal coordinate analysis (PCoA) without aged brine sample.

676

Different letters (a to c) indicate significant differences (p<0.05), while different

677

colors indicate different fermentation times. Pink and dark red indicate the 6th and 12th

678

day of fermentation, respectively. AB, aged brine; CL, chili pickle; RD, red radish

679

pickle; CP, cowpea pickle.

680

30

681

Fig. 4. LEfSe highlights consistently different bacteria taxa in different Sichuan

682

pickles. Numbers 6 and 12 indicate the 6th and 12th day of fermentation, respectively.

683

AB, aged brine; CL, chili pickle; RD, red radish pickle; CP, cowpea pickle.

684

31

685

Fig. 5. The concentrations (mg/L) of free amino acids in the different Sichuan pickles

686

during fermentation. Fresh, indicates the fresh vegetable; numbers 6 and 12 indicate

687

the 6th and 12th day of fermentation, respectively. AB, aged brine; CL, chili pickle;

688

RD, red radish pickle; CP, cowpea pickle.

689

32

690

Fig. 6. (A) PCA score and (B) loading scatter plots performed in different VOCs of

691

aged brine (asterisk), chili pickle (square), cowpea pickle (round) and RD pickle

692

(triangle). Different colors indicate different fermentation times, with pink and dark

693

red indicating the 6th and 12th day of fermentation, respectively. AB, aged brine; CL,

694

chili pickle; RD, red radish pickle; CP, cowpea pickle. The farther the compounds

695

locate from the origin in the loading plot, the more important they are for the

696

differentiation pattern.

697

33

698

Fig. 7. Correlation matrix of the Pearson rank correlation between the microbiota and

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chemical compounds in (A) chili pickle, (B) cowpea pickle and (C) radish pickle. The

700

outer circle indicates different chemical substances, while the inner circle indicates

701

different genus and species. The absolute value of the Pearson rank correlation

702

coefficient is greater than 0.7. The long red lines linking the circles represent positive

703

correlation, while the blue ones represent negative correlation. The thicker the line,

704

the stronger the correlation. The correlation coefficients and p values are shown in

705

Table S2. The serial numbers of the chemical compounds are also shown in Table S2.

706 34

707

SUPPLEMENTS

708

Table S1-1 The top thirtieth VOCs in Sichuan chili (CL) pickle during the

709

fermentation.

710

Table S1-2 The top thirtieth VOCs in Sichuan cowpea (CP) pickle during the

711

fermentation.

712

Table S1-3 The top thirtieth VOCs in Sichuan red radish (RD) pickle during the

713

fermentation.

714

Table S2 Correlation analysis between microbial flora and chemical compounds in

715

different Sichuan pickles.

716

Table S3 The total sugar, reducing sugar and soluble protein in the juices of chili,

717

radish and cowpea.

718 719

Fig. S1. The pictures of Sichuan pickles with different vegetables.

720

Fig. S2. Observed (A) and Shannon (B) curves of bacterial populations of Sichuan

721

paocai brine samples with different vegetable ingredients. Each line represents data

722

from one sample. Control, aged brine; CL, chili pickle; RD, red radish pickle; CP,

723

cowpea pickle.

724

Fig. S3. Heatmap of different classes of VOCs during the fermentation of CL pickle

725

(A), CP pickle (B) and RD pickle (C). Different color bars indicate the relative

726

abundance of each VOC class. CL, chili pickle; RD, red radish pickle; CP, cowpea

727

pickle. Fresh, indicates the fresh vegetable juice. Numbers 6 and 12 indicate the 6th

728

and 12th day of fermentation, respectively.

729

35

Highlights •

The marker bacteria and characteristic metabolites in different Sichuan pickles were ascertained.

•

The correlations between bacterial communities and chemical profiles were analyzed.

•

In Sichuan pickles, low abundance bacterial genera were significantly correlated to the metabolites, especially volatile organic compounds.

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: