The percentage of each non-
Abstract
Wine can be regarded as a nutritional source for the human diet. It contains many nutrients such as vitamins and minerals, organic acids, flavonoids, and terpenoids. The varietal aroma of wines originated from the symbionts of the grapes and epiphytic microbiota, which combinedly grew from the sexual reproduction of the plant through seeds or during clonal reproduction. Nowadays, more and more studies focus on the assembly process of epiphytic microbiota and design a synthetic microbial community based on regional characteristics to improve wine quality and biofunctions. This review synthesizes the current concepts on the construction of synthetic microbiota, analyzes the advantages and difficulties in designing a functional yeast community, and lists the practical tools for data processing and model construction. A well-designed yeast community will possess high robustness against environment interference, higher efficiency of fermentation, and higher yield of targeted bioproducts.
Keywords
- non-Saccharomyces
- top-down strategy
- systematic biology
- untargeted metabolomics
- icewine
- spontaneous fermentation
1. Introduction
The application of commercial yeast in an incompatible fermentation scenario potentially causes the risk of stuck and sluggish fermentation and product homogenization. It even has a detrimental effect on the composition of regional microbiota [1], which play important roles in characterizing aroma profile and wine quality [2]. Currently, more and more researchers and wineries have realized the limitations of commercial yeasts and are gradually turning their attention to the study of non-
Icewine is a type of dessert wine produced from grape juice extracted from frozen grapes [11]. It is normally characterized by a fruity and flowery aroma and smooth taste. Icewine making is heavily dependent on the local environmental abiotic factors during late harvest. Fully ripe grapes hang on the vines for several months to suffer natural freeze-thaw cycles and be desiccated for the concentration of sugar (>35°Brix) [12]. This period has been proven to be effective for the accumulation of varietal aroma in grapes [2, 5]. For instance, high-odor-active compounds, such as terpenes and phenylalanine-derivates, can be largely developed during late harvest through a series of interactions between epiphytic microeukaryotes and grapes [2, 5]. Our previous studies have revealed the freeze-thaw cycles are the inevitable abiotic factors that cause cellular degradation and compartmentation of grape skin [2] and improve the aroma complexity of grapes during late harvest [5]. Therefore, selecting an icewine region is the essential prerequisite for producing high-quality icewine, which must present the regional characteristics [13]. Huanren area (Liaoning Province, Northeast China) is a representative icewine-producing area in China. It is characterized by a year-round cold climate, fewer problems of pests and disease [2]. Unique ecology shapes distinctive and stress-tolerant microeukaryotic communities, which remain active in pressed grape juice and become determinant to icewine fermentation [2, 13]. However, the mechanism and driving factors of the microeukaryotic assembly process are still unclear during icewine fermentation.
Metabolomics mainly studies the variety, quantity, and change rule of the metabolites with molecular weight less than 1500 Da caused by external stimulation, pathophysiological changes, and gene mutation. It is an extension of transcriptomics and proteomics, which accurately reflect the physiological state of organisms. Therefore, it effectively reveals the biological processes of biomarkers, the mechanism of the biological activities, and the regulatory pathways [14]. In terms of detection modes, metabolomics is mainly divided into untargeted analysis and targeted analysis. The untargeted metabolomics is usually based on a high-resolution mass spectrometer (triple TOF or QE) [15]. It can perform unbiased, large-scale, and systematic detection of various metabolites in experimental samples, providing an ‘aerial photography’ perspective to reflect the metabolic disturbance in the plants and microorganisms [16].
This study jointly used untargeted metabolomics and high-throughput sequencing technology to verify the effects of the self-assembled non-
2. Materials and methods
2.1 Vinification of V. blanc icewine
Spontaneous fermentation using the epiphytic microorganisms from the fully ripe grape berries (CS): The ripe
Spontaneous fermentation using the epiphytic microorganisms from the grape berries in late harvest (HS): based on previous research,
Icewine fermentation using the self-assembled non-
No. | Yeast species | Importance | Initial ratio (%) | Inoculation (mL) |
---|---|---|---|---|
1 | 88.05 | 21.7 | 8.7 | |
2 | 80.59 | 19.8 | 7.9 | |
3 | 77.00 | 18.9 | 7.6 | |
4 | 63.69 | 15.7 | 6.3 | |
5 | 57.59 | 14.2 | 5.7 | |
6 | 39.62 | 9.7 | 3.9 |
Icewine fermentation using commercial yeast as control (BV818): a commercial yeast BV818 (AngelYeast Co., Ltd., Yichang) was inoculated into the filtrated grape juice for starting AF as control. This yeast belongs to
2.2 Viticulture and grape juice filtration
2.3 Determination of physicochemical parameters of icewine
Basic wine physicochemical parameters, such as hue, color intensity, Brix°, total sugar, total acid, glucose/fructose, lactic acid, malic acid, acetic acid, and glycerol, were determined using Y15 enzymatic autoanalyzer (Biosystems S.A., Barcelona, Spain). These analyses were performed using the appropriate enzymatic reaction kits purchased from Biosystems. Before detection, the Y-15 was calibrated with external standards that were technically supported by the Biosystems enterprise (www.biosystems.es). The pH value was measured using a PB-10 pH meter (Sartorius, Göttingen, Germany). The alcoholic degree of icewine was determined based on the National Standards of P. R. China (GB/T 15038-2006) [2, 4].
2.4 Untargeted metabolomics analysis
2.4.1 Sample collection and preparation
Icewine samples were centrifuged (4°C, 10,000 rpm, 10 min). The supernatant was added into precooled methanol/acetonitrile/water solution (2:2:1, v/v). The mixture was carried out vortex, ultrasonic homogenization (4°C, 30 min), stewing (−20°C, 10 min), and centrifugation (4°C, 12,000 rpm, 20 min). Then, the supernatant was conducted vacuum freeze-drying. Before metabolomics analysis, 100 μL acetonitrile aqueous solution (acetonitrile:water = 1:1, v/v) was added to redissolve the dry sample. Subsequently, the solution was vortexed and centrifuged (4°C, 12,000 rpm, 15 min) for UHPLC-MS analysis. Twenty microliters of each sample were mixed as a QC sample [17].
2.4.2 UHPLC conditions
Analyses were performed using a UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight (AB Sciex Triple TOF 6600). For HILIC separation, samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 μm column (waters, Ireland). In both ESI positive and negative modes, the mobile phase contained A = 25 mM ammonium acetate and 25 mM ammonium hydroxide in water, and B = acetonitrile. The gradient was 85% B for 1 min, linearly reduced to 65% in 11 min, then reduced to 40% in 0.1 min, kept for 4 min, and then increased to 85% in 0.1 min, with a 5 min re-equilibration period employed.
2.4.3 ESI-Q-TOF MS/MS conditions
The ESI source conditions were set as follows: Ion Source Gas1 (Gas1) as 60, Ion Source Gas2 (Gas2) as 60, curtain gas (CUR) as 30, source temperature: 600°C, IonSpray Voltage Floating (ISVF) ± 5500 V. In MS-only acquisition, the instrument was set to acquire over the
2.4.4 Data processing
The raw MS data (wiff.scan files) were converted to MzXML files using ProteoWizard MSConvert before importing them into freely available XCMS software. CAMERA (Collection of Algorithms of Metabolite Profile Annotation) was used for the annotation of isotopes and adducts [18]. In the extracted ion features, only the variables having more than 50% of the nonzero measurement values in at least one group were kept. Compound identification of metabolites was performed by comparing the accuracy
2.5 Metabarcoding of internal transcribed spacer (ITS) sequence
The quantity and quality of extracted DNA were measured using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. To analyze the taxonomic composition of the microeukaryotes communities, ITS1 of microeukaryotes 18S rRNA genes via a two-step amplification procedure using primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′), and ITS1R (5′-GCTGCGTTCTTCATCGATGC-3′) were carried out. Specific DNA extraction, PCR, and Illumina MiSeq sequencing (2- by 150-bp reads) were performed using the Illumina MiSeq platform at Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China) [4]. Each sample was extracted for use in three replicates, and each extraction was analyzed twice. The Quantitative Insights into Microbial Ecology (QIIME2) pipeline was employed to process the sequencing data. Amplicon sequence variants were obtained using the procedure of denoise, quality control, splicing, and mosaicking, which were carried out using denoise-paired QIIME DADA2.
2.6 Statistical analysis
Statistics for the characteristic metabolites in
3. Results
3.1 Identification of the characteristic metabolites
The results of PCA and heatmap cluster of metabolomic illustrated different metabolite profiles between the four fermentation scenarios and
Partial least squares discrimination analysis (PLS-DA) was carried out to compare the differences in each fermentation scenario (Figure 2B). Component 1 and component 2 contributed 76.6% of PLS-DA in total. It indicated the metabolite profile of each fermentation can be discriminated with the first two components. The four fermentations showed a far distance from the grape juice. Herein, the spontaneous fermentations (HS and CS) were close to HN, while BV818 showed a different metabolite profile from the others. To study the characteristic metabolites and their correlations to each fermentation. The top 20 characteristic metabolites were ranked according to their VIP scores of PLS-DA (Figure 2C). Most characteristic metabolites in icewine were positively correlated to the spontaneous fermentation and HN except glycerophosphocholine, succinate, and neohesperidose. In terms of spontaneous fermentation, some characteristic metabolites commonly showed positive correlations to CS and HS, such as d-gluconate, trans-3′-hydroxycotinine o-β-d-glucuronide, d-galacturonic acid, 2-isopropylmalic acid, gramine, adenine, and two dipeptides (Leu-His and Thr-Leu).
3.2 Spatial dispersal of microeukaryotic communities and correlations to the characteristic metabolites during AF
The spatial dispersal and co-occurrence network of each fermentation scenario jointly showed the dynamic changes of microeukaryotes during AF (Figure 3). Accordingly, the microeukaryotes in BV818 were highly developed at the end stage of AF, where ASVs of the microeukaryotes showed a higher enrichment but lower diffusivity (Figure 3A). The inner interactions between microeukaryotes in each module were positive. The major microeukaryotic modules in BV818 were modules 1, 2, 9, 7, and 14. Herein, the cross interactions between modules 1, 2, 7, and 9 showed higher negative similarity. It indicated the major activities between these modules were dominated by negative cohesion, such as competition, parasitism, or amensalism [21]. Moreover, module 14 showed fewer interactions with other modules. It indicated the related microeukaryotic communities could independently develop and be neutralized to other species during AF. HN showed a contrary spatial dispersal but a similar co-occurrence network to BV818 (Figure 3C). The strongest diffusivity of microeukaryotes was observed at the beginning of AF, while the weakest diffusivity was presented at the end of AF. The major microeukaryotic modules of HN were 1, 2, 6, and 12. The cross interactions between modules 1, 2, and 6 were also dominated by negative cohesion; meanwhile, these modules showed fewer interactions with module 12. Both CS and HS belonging to spontaneous fermentation and the same microbial origination from a vineyard resulted in similar spatial dispersals and co-occurrence networks of microeukaryotes during AF (Figure 3B and D). ASVs of microeukaryotes were evenly distributed in all the stages of AF. Comparatively, the microeukaryotes of CS mainly developed at the beginning and end stages of AF, while the microeukaryotes’ development of HS was vigorous at the middle stage of AF. The spontaneous fermentation presented more modules and more complex interactions than BV818 and HN. The major microeukaryotic modules in CS were 1, 2, 6, 8, 10, and 15. Herein, module 1 and module 10 showed positive cross interactions, namely, cooperation and mutualism. However, other microeukaryotic modules were mainly controlled by negative cohesion. The major modules in HS were 1, 2, 6, 7, 9, and 10. Herein, module 1 and module 7 showed positive cross interactions, while other modules were controlled by negative cohesion. In general, the cross-interaction between microeukaryotic phyla during AF was dominated by negative cohesion, and the abundance patterns of microeukaryotic communities depend on different fermentation modes [22].
Mantel test was used to study the correlations between the specific order of microeukaryotes and fermentation mode (Figure 4A). As a result,
The correlation matrix between the functional compounds and the top 25 active microeukaryotic genera was established using a random forest model and Pearson’s correlation coefficients (Figure 4B). Accordingly, many microeukaryotes showed positive correlations. They potentially contributed to the development of terpenoids and flavonoids in icewines, such as
3.3 Characteristic metabolites of V. blanc grape induced stochastic assembly of microeukaryotic communities during AF
One of the reasons why spontaneous fermentation could not be widely applied is that many winemakers treat it as a ‘black-box’, a complex, multi-strains participated system. With the development of systematic biology, the neutral community model (NCM) will be a useful tool to excavate the deep principle of microbial assembly in co-fermentation or spontaneous fermentation [23]. In this study, the NCM predicted a large fraction of the total correlation between the occurrence frequency of ASVs and their relative abundance variations (Figure 5A), with 83%, 73%, 80%, and 76% of the explained microeukaryotic community variations for the BV818, CS, HN, and HS, respectively. The NCM indicates that microeukaryotic communities comply with stochastic assembly instead of deterministic assembly in different fermentation scenarios. Higher R2 suggests the sample is closer to the NCM. Compared to spontaneous fermentation, commercial yeast inoculation (BV818) fits the stochastic process. Moreover, a smaller Nm value suggests more restrictive species dispersal. Therefore, the level of species dispersal in BV818 (Nm = 374) was the lowest in all fermentations, while HS had the maximum species dispersal (Nm = 1005). CS and HN showed similar Nm values. This result is also in accord with the spatial dispersal of microeukaryotic communities during AF; namely, the microeukaryotic communities of HS can evenly develop at three stages of AF, while the ASVs of microeukaryotes of BV818 are mainly concentrated at the end of AF.
β-Nearest taxon index (β-NTI) between samples was calculated to describe the phylogenetic turnover of microeukaryotic communities in the different fermentation scenarios. Linear regression curves between |β-NTI| and the relative abundance of the six characteristic metabolites were constructed (Figure 5B). Among these six characteristic metabolites, β-sitosterol is a plant steroid, empenthrin, gentiopicroside, zerumbone, and qingyangshengenin belong to terpenoids, and phlorizin is a kind of flavonoids. Empenthrin, β-sitosterol, gentiopicroside, phlorizin, and zerumbone positively correlated to |β-NTI| while gentiopicroside and qingyangshengenin showed negative correlations to |β-NTI|. However, β-NTI of all the fermentations were in the range of −2 to 2 (Figure 5C). Although gentiopicroside and qingyangshengenin could affect the stochasticity of microeukaryotic communities, the stochastic process still dominated the phylogenetic turnover of microeukaryotic communities during AF. To assess the relative importance of determinism and stochasticity, modified stochasticity ratio (MST) was carried out to supply the results of NCM (Figure 5D). In four fermentation scenarios, MST of CS (36.1%) was much lower than the boundary (50%), which indicated determinism affected the microeukaryotic assembly of CS. The MST of HS (54.7%) was slightly higher than the MST of HN (53.0%) without a significant difference (
3.4 The specific process of stochastic assembly of microeukaryotic communities and the selection of fermentative specialists
Principal coordinates analysis (PCoA) was carried out to observe the degree of similarity between the microeukaryotic communities of different fermentation scenarios (Figure 6A). The total contribution of the first two components of PCoA can explain 66.11% of variations. The confidence intervals of HN and BV818 have been separated from the spontaneous fermentations (CS and HS). Comparatively, the degree of distribution of BV818 was more concentrated than that of the others while HN and HS showed higher degree of distributions than CS and BV818. The result of PCoA indicated that the microeukaryotic composition of BV818 was different from co-fermentations (HN, CS, and HS), and the concentrated distribution of BV818 also indicated low diversity of microbial participation during AF. Moreover, the top five abundant microeukaryotic genera were selected and located on the PCoA plot. Herein,
Except for the NCM, phylogenetic-bin-based null model analysis was carried out further to infer community assembly mechanisms in the stochastic process (Figure 6B) [20]. The important assembly processes of microeukaryotic communities were heterogeneous selection, homogeneous selection, dispersal limitation, homogenizing dispersal, and drift. Specifically, the assembly process was mainly composed of homogeneous selection (43.1%) and drift (56.2%) in BV818. For HN, dispersal limitation (0.05%), homogeneous selection (45.1%), and drift (49.7%) were the important parts of the stochastic assembly. For CS, the specific stochastic assembly included homogeneous selection (25.5%), dispersal limitation (0.05%), homogenizing dispersal (0.04%), and drift (65.4%). The heterogeneous selection was the characteristic assembly process in HS, which possessed 0.05% of the stochastic assembly. Besides, the proportion of homogeneous selection, dispersal limitation, homogenizing dispersal, and drift was 15.7%, 10.5%, 0.03%, and 65.4% of the stochastic assembly in HS, respectively. In general, drift and homogeneous selection were the major assembly processes during AF of icewine, and HS showed a more complex composition of the assembly process than other fermentations. Partitioning β-diversity of richness was carried out to quantify the result of species replacement between different fermentations (turnover) and species gains or losses between different microeukaryotic communities (nestedness) (Figure 6C). As a result, species replacement played an important role in shaping microeukaryotic communities. The greatest difference between the contributions of β-diversity components was observed in the four fermentation scenarios, where the turnover was about six times higher than nestedness. Specifically, the range of turnover in each fermentation was from 50% to 55% while the range of nestedness was from 5% to 8%. The nestedness of HN was the highest level among the four fermentations. For turnover, BV818 and CS were higher than HN and HS.
To inspect the distribution of OTUs from each fermentation and the specificity of these microeukaryotes during AF, specificity and occupancy were calculated for each OUT, which was then projected onto a plot (SPEC-OCCU plot, Figure 6D). As indicated by the spread of OTUs across occupancy, OTUs from BV818 and HN communities showed highly varied occupancy while the majority of OTUs from CS and HS exhibited more homogenous occupancy, which mainly concentrated at the range of 0–0.25. To find specialist species attributable to each fermentation, we selected OTUs with specificity and occupancy greater or equal to 0.7 (dotted boxes). These microeukaryotes are specific and could be highly fermentative in their fermentation scenarios. The number of these specialist OTUs was BV818 (3 OTUs represent), HN (6 OTUs), CS (2 OTUs), and HS (3 OTUs), respectively. Specifically,
3.5 The analysis of metabolic pathways in four icewine fermentation scenarios
A structural equation modeling (SEM) accounting for the yeast strains, characteristic metabolites, and key assembly factors was built to analyze the interactions of variables in four icewine fermentation scenarios (Figure 7A). Based on the previous results, five yeast strains (
The top 20 enriched KEGG pathways of four icewine fermentations were shown in Figure 7B, where the rich factors of each KEGG pathway were the ratio of differential genes in this pathway, while the p-value indicated the importance of this pathway in the icewine fermentation. Accordingly, protein digestion, absorption, and ABC transporters were the most important pathways of microeukaryotic metabolism in all fermentation treatments. Compared to commercial yeast, CS, HN, and HS showed similar categories of important KEGG pathways, which also contained mineral absorption, biosynthesis of amino acids, and aminoacyl-tRNA biosynthesis. Lysine degradation was the characteristic pathway for the spontaneous fermentations (CS and HS). Starch and sucrose metabolism were relatively important to HS and BV818, while proximal tubule bicarbonate reclamation was the common pathway in BV818 and CS. Interestingly, valine, leucin, and isoleucine biosynthesis and nicotinate and nicotinamide metabolism were the specific pathways in CS. It indicated more potential volatile metabolites could be synthesized by applying the indigenous yeast community during alcoholic fermentation.
4. Discussion
In this study, the top-down approach was first used to design a self-assembled non-
The spatial dispersal and co-occurrence network of each fermentation scenario performed a dynamic distribution of microeukaryotes and the correlations between microeukaryotic communities during AF. As a result, negative cohesion, such as competition, parasitism, or amensalism, played an important role in the cross-interactions between microeukaryotic communities. The microeukaryotic dispersal of BV818 was mainly concentrated at the end of fermentation, while HN showed the opposite result that the microeukaryotes mainly distributed at the beginning of fermentation. Understandably, most commercial yeasts were
People had been drinking natural wines for thousands of years before sulfur dioxide was applied in the wine industry. Current studies have admitted the importance of a certain strain of commercial yeast in the winemaking industry, while indigenous fungi have been misunderstood as microbial contaminators during AF. In our study, the dominator of AF was indigenous non-
5. Conclusion
This study first explored the assembly process of microeukaryotic communities in four typical icewine fermentation scenarios. Herein, top-down design was carried out to construct a non-
Acknowledgments
We acknowledge Huanren Senpatina Icewine Domaine Co., Ltd. for kindly providing the experimental raw materials.
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