ORIGINAL RESEARCH article
Shotgun Metagenomics of a Water Kefir Fermentation Ecosystem Reveals a Novel Oenococcus Species
- Research Group of Industrial Microbiology and Food Biotechnology (IMDO), Faculty of Sciences and Bioengineering Sciences, Vrije Universiteit Brussel, Brussels, Belgium
Water kefir is a fruity, sour, slightly alcoholic and carbonated beverage, which is made by fermentation of an aqueous sucrose solution in the presence of dried figs and water kefir grains. These polysaccharide grains contain lactic acid bacteria (LAB), yeasts, and sometimes bifidobacteria and/or acetic acid bacteria, which consume sucrose to produce exopolysaccharides, lactic acid, acetic acid, ethanol, and carbon dioxide. Shotgun metagenomic sequencing was used to examine the microbial species diversity present at two time points during water kefir fermentation in detail, both in the water kefir liquor and on the water kefir grains, hence representing four samples. Lactobacillus harbinensis, Lactobacillus hilgardii, Lactobacillus nagelii, Lactobacillus paracasei, and a Lactobacillus species similar to Lactobacillus hordei/mali were present in the water kefir examined, along with Bifidobacterium aquikefiri and two yeast species, namely Saccharomyces cerevisiae and Dekkera bruxellensis. In addition, evidence for a novel Oenococcus species related to Oenococcus oeni and Oenococcus kitaharae was found. Its genome was derived from the metagenome and made available under the name of Candidatus Oenococcus aquikefiri. Through functional analysis of the four metagenomic data sets, it was possible to link the production of lactic acid, acetic acid, ethanol, and carbon dioxide to subgroups of the microbial species found. In particular, the production of mannitol from fructose was linked to L. hilgardii, Candidatus O. aquikefiri, and B. aquikefiri, whereas glycerol production was associated with S. cerevisiae. Also, there were indications of cross-feeding, for instance in the case of amino acid supply. Few bacterial species could synthesize a limited number of cofactors, making them reliant on the figs or S. cerevisiae. The LAB species in turn were found to be capable of contributing to water kefir grain growth, as dextransucrase-encoding genes were attributed to L. hilgardii, L. hordei/mali, and Candidatus O. aquikefiri.
Water kefir is a fruity, sour, and slightly alcoholic and carbonated, fermented beverage produced by adding water kefir grains to a sucrose solution containing dried figs, and letting the mixture to ferment for 2–4 days at room temperature (De Roos and De Vuyst, 2018). As water kefir fermentations are typically performed at household-level, the conditions vary between fermentations. Most people ferment water kefir at 21–25°C for 24–72 h, using between 6 and 10% sucrose and 6–31% water kefir grains (Gulitz et al., 2011, 2013; Marsh et al., 2013; Laureys and De Vuyst, 2014, 2017; Laureys et al., 2018). The polysaccharide water kefir grains harbor the bacteria and yeasts and part of the water kefir grains is re-used after sieving of the water kefir batch, representing a backslopping process (Laureys and De Vuyst, 2014, 2017). These microorganisms carry out the fermentation of sucrose, thereby producing exopolysaccharides, which are necessary for water kefir grain growth, and lactic acid, acetic acid, ethanol, carbon dioxide, and aroma compounds that give water kefir its flavor and texture.
The water kefir grain microbiota consist of lactic acid bacteria (LAB), mainly Lactobacillus species, such as Lactobacillus casei/paracasei, Lactobacillus hilgardii, and Lactobacillus nagelii, and yeasts, frequently Saccharomyces cerevisiae, sometimes bifidobacteria, in particular Bifidobacterium aquikefiri, and as a minority also acetic acid bacteria, for instance Acetobacter fabarum, the latter especially upon extended fermentation or fermentation in the presence of oxygen (Magalhães et al., 2010; Gulitz et al., 2011, 2013; Marsh et al., 2013; Laureys and De Vuyst, 2014, 2017; Zanirati et al., 2015; Laureys et al., 2016, 2018). In general, water kefir grains are reminiscent of milk kefir grains, which are also consortia of the same major groups of microorganisms in a polysaccharide matrix (De Roos and De Vuyst, 2018). However, milk kefir grains can contain species rarely or not at all found in water kefir, such as Lactobacillus kefiranofaciens, which is responsible for the biosynthesis of the heteropolysaccharide kefiran, the main component of milk kefir grains (Prado et al., 2015). In contrast, the main component of water kefir grains is a homopolysaccharide, dextran, produced by L. hilgardii (Pidoux, 1989; Waldherr et al., 2010; Laureys and De Vuyst, 2017).
Previous studies on the microbial species diversity of water kefir grains relied on culture-dependent as well as culture-independent techniques, the latter including amplicon sequencing of the V1–V4 and V4–V5 regions of the 16S rRNA gene using metagenomic DNA (Gulitz et al., 2013; Marsh et al., 2013). These metabarcoding studies have indicated the existence of a non-identified Bifidobacterium species, which was later identified as B. aquikefiri (Gulitz et al., 2013; Laureys et al., 2016), and the predominance of Zymomonas species in certain water kefirs (Marsh et al., 2013). However, high-throughput amplicon sequencing often lacks accuracy at the species level, especially when compared to shotgun metagenomic sequencing (Ranjan et al., 2016). Shotgun metagenomic sequencing is witnessing an increased use to unravel the composition of microbial ecosystems involved in food fermentations. So far, it has been employed to investigate the spontaneous fermentation processes of kimchi (Jung et al., 2011), cocoa (Illeghems et al., 2012), puer tea (Lyu et al., 2013; Li et al., 2018), wine (Sternes et al., 2017), sausage (Ferrocino et al., 2018), and beer (Smukowski Heil et al., 2018), as well as of the ecosystem composition of various cheeses (Wolfe et al., 2014; Dugat-Bony et al., 2015; Escobar-Zepeda et al., 2016; Duru et al., 2018), milk kefirs (Nalbantoglu et al., 2014; Walsh et al., 2016, 2018), withered Corvina grapes (Salvetti et al., 2016), an Indian rice wine starter culture (Bora et al., 2016), cereal vinegar (Wu et al., 2017), and a fermented dairy beverage nunu (Walsh et al., 2017). As opposed to amplicon sequencing, shotgun metagenomic sequencing data can further be used to infer potential metabolic functions encoded by the genomes of the members of the ecosystem under study through the assembly of the metagenomic sequence reads followed by gene prediction (De Filippo et al., 2012; Prakash and Taylor, 2012), as has been employed in the cases of cocoa bean fermentation (Illeghems et al., 2015), Mexican ripened cheese (Escobar-Zepeda et al., 2016), cereal vinegar (Wu et al., 2017), sausage fermentation (Ferrocino et al., 2018), and puer tea fermentation (Li et al., 2018). Finally, shotgun metagenomic sequencing analysis of fermented food samples allows the discovery of whole genomes of microorganisms that were not cultured before (Duru et al., 2018; Smukowski Heil et al., 2018).
The aim of this study was to examine in detail the water kefir fermentation ecosystem through shotgun metagenomic sequencing, using both water kefir liquor and water kefir grains at two different time points during the fermentation process to examine whether or not all microbial groups and/or species present were found through culturing before and to enable evaluating changes of these microbial communities over time. The four shotgun metagenomic sequence data sets were used to provide not only a deeper insight into the water kefir ecosystem’s microbial species composition but also to seek its yet undiscovered functional potential, and finally link the potential functions to each member of the ecosystem.
Materials and Methods
Water Kefir Fermentation and Sampling
The water kefir fermentation experiment that was initiated by water kefir grains received from a private individual from Lokeren, Belgium, and that was previously assessed as to its microbiology and metabolomics as described before (Laureys and De Vuyst, 2017), was sampled for a metagenomic analysis in the present study. Briefly, a water kefir simulation medium containing unrefined cane sugar (7.1%, m/v) and fig extract (17.6%, v/v) was used for the water kefir fermentations. These were carried out in 100 ml glass bottles containing 85 ml of water kefir simulation medium and 15 g of water kefir grains at 21°C for up to 192 h. At each time point of fermentation, three bottles were removed, the water kefirs were sieved, and the water kefir grains were washed with sterile saline (0.85%, m/v, NaCl; Merck, Darmstadt, Germany) and stored at -20°C. The liquors were centrifuged (7,200 × g, 20 min, 4°C) and the cell pellets obtained were also stored at -20°C. Time points 24 and 72 h were selected for metagenomic analysis, based on the outcome of previous studies on these water kefir fermentation processes (Laureys and De Vuyst, 2017).
Metagenomic DNA Extraction
Metagenomic DNA was extracted from both water kefir liquor pellets and water kefir grains of the samples mentioned above, which resulted in four metagenomes. Pellets from 40 ml of water kefir liquor or 2.2 g of water kefir grains (in 11 aliquots of 0.2 g, due to the high volume of water kefir grains) were used as input material for the DNA extraction protocol that was carried out in tubes. Actual DNA extraction was performed based on a method combining enzymatic, chemical, and mechanical cell lysis, followed by phenol/chloroform/isoamylalcohol extraction and column purification of the DNA, as described previously (Vermote et al., 2018). However, the cell lysis steps also included polysaccharide removal, which was undertaken after the proteinase K treatment and before the chloroform/phenol/isoamylalcohol DNA extraction by adding 100 μl of 5 M NaCl (Merck), incubating the suspension at 65°C for 2 min, adding 80 μl of 10% (m/v) hexadecyltrimethylammonium bromide (Calbiochem, San Diego, CA, United States), and incubating at 65°C for 10 min. Also, in the case of the water kefir grains, the aqueous phases obtained after extraction with chloroform/phenol/isoamyl alcohol from the 11 aliquots were pooled and used for DNA purification.
Preparation of Libraries, Shotgun Metagenomic Sequencing, and Data Preprocessing
All materials and apparatus were from Thermo Fisher Scientific (Wilmington, DE, United States), unless stated otherwise. After optimizing the shearing time (7, 11, or 15 min), the metagenomic DNA was enzymatically sheared to produce library fragments of the desired length, using an Ion Xpress Plus Fragment Library Kit with 100 ng of DNA as input, and applying the optimal shearing time. The sheared DNA was purified using an Agencourt AMPure XP Kit (Beckman Coulter, Brea, CA, United States), followed by adapter ligation and nick repair, and purification of the adapter-ligated DNA using an Agencourt AMPure XP Kit. The unamplified library was size-selected using an E-Gel SizeSelect 2% agarose gel to produce library fragments of approximately 330 bp. The size-selected library was qualified using a Bioanalyzer 2100 with an Agilent High Sensitivity DNA Kit and quantified using an Ion Library Quantitation Kit based on quantitative PCR. As such, four 200-base-read libraries were prepared for sequencing, further referred to as L24, G24, L72, and G72, where L and G denote the type of input material, i.e., water kefir liquor pellets and water kefir grains, respectively, and the Arab numerals denote the time point, either after 24 or 72 h of water kefir fermentation.
The size-selected library was used as template for emulsion PCR onto Ion Sphere Particles (ISPs), using an Ion PGM Template OT2 400 Kit and the Ion OneTouch 2 Instrument. The template-positive ISPs were enriched using the Ion PGM Enrichment Beads and an Ion OneTouch ES. Template-positive ISPs were loaded on an Ion 316 Chip and sequencing was performed using the Ion Hi-Q Sequencing Kit on an Ion PGM.
The metagenomic sequence data were subjected to quality checks and quality trimming using FastQC v0.10.1 (Andrews, 2010) and PRINSEQ 0.20.2 (Schmieder and Edwards, 2011). All four metagenomic data sets were submitted to the European Nucleotide Archive of the European Bioinformatics Institute (ENA/EBI) and are accessible under the study accession number PRJEB21603.
Taxonomic Analysis of the Metagenomic Sequence Data
Taxonomic Analysis Using All Metagenomic Sequence Reads
The quality-checked metagenomic sequence reads were used to assess the taxonomic composition of the water kefir fermentation samples, using diverse taxonomy profiling tools, namely BLAST (Altschul et al., 1990), Kraken (Wood and Salzberg, 2014), and Kaiju (Menzel et al., 2016).
The BLAST algorithm blastn was used to align the metagenomic sequence reads to sequences in the non-redundant nucleotide (nt) database of the National Center for Biotechnology Information (NCBI, Bethesda, MA, United States) and in a custom-made database consisting of bacterial, archaeal, and fungal genome sequences obtained from RefSeq (NCBI; Tatusova et al., 2014). The BLAST algorithm blastx was used to align the metagenomic sequence reads to sequences in the non-redundant protein (nr) database of the NCBI. The BLAST output was parsed with MEGAN 5.7.0 (Huson et al., 2011), using the following settings: MinScore, 100; MaxExpected, 0.01; TopPercent, 10.0; MinSupport, 150; and LCA percent [i.e., a naive lowest common ancestor (LCA) algorithm], 100. The output from MEGAN was further processed with R (R Core Team, 2017) in RStudio (RStudio Team, 2015), using the packages biom (McMurdie and The Biom-Format Team, 2014), RJSONIO (Temple Lang, 2014), reshape2 (Wickham, 2007), ggplot2 (Wickham, 2009), and RColorBrewer (Neuwirth, 2014).
A custom-made database consisting of archaeal, bacterial, and fungal genome assemblies from type material from the NCBI Assembly database was constructed and used for sequence classification with Kraken. A database consisting of bacterial and microbial eukaryotic protein sequences from nr was used for sequence classification with Kaiju.
Taxonomic Analysis Based on DNA Marker Genes
MetaPhlAn2 was used to estimate the composition of the microbial communities during water kefir fermentation based on a comparison of the metagenomic sequence reads to a database consisting of unique clade-specific marker genes (Truong et al., 2015).
In addition, bacterial and archaeal 16S rRNA gene fragments were extracted from the metagenomic data sets with rRNASelector (Lee et al., 2011), and sequence fragments of the internal transcribed spacer sequences ITS1 and ITS2 were extracted with ITSx 1.0.11 (Bengtsson-Palme et al., 2013), using the models for fungi, animals, oomycetes, and higher plants. The former reads were binned using RDP Classifier (Wang et al., 2007), which was trained beforehand with training set No. 14, whereas the latter ones were aligned to the nt database using blastn. The results of both methods were parsed with MEGAN 5.7.0, using the following settings: MinScore, 80; MaxExpected, 0.01; TopPercent, 100; and MinSupport, 10 for the RDP Classifier output; and MinScore, 100; MaxExpected, 0.01; TopPercent, 10; and MinSupport, 10 for the BLAST output.
Taxonomic Analysis Based on Metagenomic Recruitment Plotting
To construct metagenomic recruitment plots for species-level taxonomic analysis, genera represented by more than 0.1% of all reads in any of the four metagenomes with any BLAST-based method used were selected. Of all species and subspecies of these genera, the genome sequences of the sequenced type strains were obtained from the NCBI RefSeq assembly database. Another representative strain was chosen, preferably one with a complete genome, if the genome sequence of the type strain of a species was not available. The list of the genome assemblies is represented in Supplementary Table S1. A BLAST search was performed using blastn, using the metagenomic sequence reads as query sequences and the collected genome sequences as database. The minimum identity threshold was set to 60%. Only the top hit for each sequence was retained, using 60% as the minimum query coverage threshold. The result was used as a basis for metagenomic recruitment plotting, using R, RStudio, and the R packages ggplot2, reshape2, and scales (Wickham, 2016).
Overall Taxonomic Analysis Based on Tools Using All Metagenomic Sequence Reads and Metagenomic Recruitment Plotting
The results of the bioinformatic analyses using BLAST, Kraken, Kaiju, and metagenomic recruitment plotting were extracted “per read.” Each read was assigned to a taxon by the tools used, in the following order of priority: Kaiju, Kraken, blastn versus nt, blastn versus RefSeq, and blastx versus nr. The genus-level identifications from reads that belonged to the ranks “subspecies,” “species,” and “species group” were extracted from the taxon name and added to the genus-level identifications. Reads from other ranks were summarized in the category “above genus.” Categories represented by less than 0.5% of all reads were summarized in the category “minorities.” Temporal shifts in the microbial communities of the water kefir liquor and the water kefir grains were assessed using the results of the overall taxonomic analysis.
Metagenomic Sequence Read Assembly, Metagenomic Contig Binning, and Functional Analysis
Assembly of the metagenomic sequence reads into contigs was performed for the four metagenomes separately, as well as for a combined data set comprising all metagenomic sequence reads of the four data sets (co-assembly), using the MEGAHIT assembler (Li et al., 2015), with the preset parameter set “meta sensitive.” Only contigs longer than 1000 bp were retained. Assembly statistics, including total size of the assembly (i.e., the sum of contig lengths), the number of contigs, the longest contig length, the average contig length, the median contig length, and the N50 value were calculated using an in-house Python script. The metagenomic contigs assembled were annotated with Prokka (Seemann, 2014). The Prokka annotations were the basis for the targeted search of relevant metabolic pathways. To facilitate the search for carbohydrate-active enzymes, the predicted products of coding sequences found in the contigs were screened with HMMER 3.1b11 using the dbCAN database (Yin et al., 2012), and using CAZy (Lombard et al., 2014) and CAZypedia, the encyclopedia of carbohydrate-active enzymes2, as references. The features that were found during the examination of the contigs were assigned to corresponding taxa through binning of the metagenomic contigs with CONCOCT (Alneberg et al., 2014) and mapping the contigs or contig fragments produced during binning with BWA-MEM (Li, 2013) to the genome assemblies of species found in the taxonomic analysis. The quality of the resulting bins was assessed with CheckM (Parks et al., 2015).
Taxonomic Characterization of a Novel Oenococcus Species
The taxonomic position of a novel Oenococcus species detected through the taxonomic analyses of the metagenomes was further characterized. First, the contigs that were assigned by BWA-MEM to species other than Oenococcus alcoholitolerans, Oenococcus kitaharae, and Oenococcus oeni but that were clustered into Oenococcus metagenomic bins with CONCOCT were discarded, except if they exhibited similarity to any sequence belonging to the genus Oenococcus in the NCBI nt database using the blastn algorithm. Then, the contigs carrying rRNA genes related to Oenococcus were added to the corresponding bins. These metagenome-assembled genomes (MAGs) were reannotated with Prokka and their assembly statistics were calculated as described above. Based on the assembly statistics, the Oenococcus MAG derived from the metagenomic data set L24 was subjected to an assessment with CheckM once more and retained for further analyses. It was uploaded to ENA/EBI and is available under the study accession number PRJEB29525. The protein sequences derived from this MAG, as well as the protein sequences from the genomes of 17 O. oeni strains, two O. kitaharae strains, one O. alcoholitolerans strain, and from Lactococcus garvieae ATCC 49156, the latter serving as an outgroup, were used in the analysis with OrthoFinder 1.1.10, making use of DIAMOND as alignment tool (Buchfink et al., 2015; Emms and Kelly, 2015). The resulting set of 326 single-copy core protein sequences was subjected to a multiple sequence alignment using MUSCLE (Edgar, 2004). After trimming, the alignments with trimAl (Capella-Gutierrez et al., 2009) without allowing gaps and concatenating the alignments by using an in-house Python script, a rooted phylogenetic tree was constructed using FastTree (Price et al., 2010). Additionally, the average nucleotide identity (ANI) of the genome assemblies used was calculated with the OrthoANIu tool (Yoon et al., 2017), followed by visualization using FigTree3 and the R package ggtree (R Core Team, 2017; Yu et al., 2017).
The successful sequencing of the four metagenomic libraries derived from a water kefir fermentation process [two liquor (L) and two grain (G) pellet samples], obtained as a function of time (after 24 and 72 h of fermentation), resulted in four quality-trimmed data sets with a combined size of approximately 1.86 Gbp and with read lengths between 20 and 260 bp (Table 1).
Table 1. Statistics of the four metagenomic data sets derived after quality trimming from samples of a water kefir fermentation process as a function of time.
Taxonomic Analysis of the Water Kefir Metagenomes
Taxonomic Analysis Using All Metagenomic Sequence Reads
Applying different taxonomy profiling methods to assess the microbial composition of the water kefir fermentation samples resulted in slightly differing taxa and varying percentages of reads assigned to them (Figure 1). The highest number of metagenomic sequence reads were assigned to specific taxa using Kaiju (more than 50% of all reads), whereas the lowest number of reads was assigned using blastx versus nr. Up to 27.2% of all reads were assigned to the genus Lactobacillus, and up to 13.5 and 11.0% of all reads to the genera Bifidobacterium and Oenococcus, respectively. The genus Pediococcus was found as a minority in all cases, except for the sample L72 using blastn versus RefSeq (0.55% of all reads). Cellulosimicrobium was found in samples G24 and G72, albeit with much fewer reads in the latter (up to 1.3 and 0.01% of all reads, respectively). Among the yeasts, Saccharomyces and Dekkera were found with all methods, except for Kraken that did not detect Dekkera.
Figure 1. Percentage of metagenomic sequence reads from water kefir fermentation metagenomes assigned to different genera using BLAST [NCBI nucleotide database (nt), RefSeq, or NCBI protein database (nr)], Kraken, Kaiju, metagenomic recruitment plotting (MGRec), or an overall taxonomic analysis (ALL). Actinobacteria are colored in shades of red/orange, Firmicutes in shades of blue, and yeasts in shades of green. The category “minorities” includes all genera that were represented by less than 0.5% of all reads. The category “above genus” represents all assigned taxonomic levels higher than the genus level.
Taxonomic Analysis Based on DNA Marker Genes
MetaPhlAn2 estimated that more than half of the microbial communities was represented by species of the genus Lactobacillus, followed by Saccharomyces (relative abundance of ca. 25%) and Oenococcus (relative abundance of less than 10%; Figure 2A). MetaPhlAn2 did not find Dekkera, but it predicted the presence of Naumovozyma and unclassified Debaryomycetaceae.
Figure 2. (A) Microbial community structure of the water kefir fermentation samples as estimated using MetaPhlAn2, expressed as relative abundances of the microorganisms identified. (B) Percentage of metagenomic sequence reads classified into a taxon in the water kefir fermentation metagenomes based on the rRNA gene-related metagenomic sequence reads classified with RDP Classifier. (C) Percentage of metagenomic sequence reads classified into a taxon in the water kefir fermentation metagenomes based on the reads from the internal transcribed spacer (ITS) regions classified by means of blastn versus nt. The category “minorities” includes genera with a relative abundance lower than 0.5%. The categories “above genus” and “above species” represent all taxonomic levels assigned higher than the genus level or the species level, respectively.
Reads related to the 16S rRNA gene, which were extracted from the metagenomes with rRNASelector and classified with RDP Classifier (Figure 2B), indicated the presence of Lactobacillus, Oenococcus, and Bifidobacterium in the four fermentation samples, as well as Cellulosimicrobium and Pediococcus in some of them. Similarly, the presence of S. cerevisiae and D. bruxellensis was supported by reads related to ITS1 and ITS2, extracted from the metagenomes with ITSx and aligned to the nt database (Figure 2C).
Taxonomic Analysis Based on Metagenomic Recruitment Plotting
To assess the microbial composition of the water kefir ecosystem at the species level through metagenomic recruitment plotting, the metagenomic sequence reads were aligned to a custom-made database consisting of genome sequences of all type strains of those genera that were represented by more than 0.1% of all reads in any of the four metagenomes with any of the three BLAST-based methods applied, namely Bacillus, Bifidobacterium, Cellulomonas, Cellulosimicrobium, Isoptericola, Lactobacillus, Leuconostoc, Oenococcus, Pediococcus, Sanguibacter, Dekkera, and Saccharomyces.
For the data sets L24, G24, L72, and G72, 87.2, 90.4, 88.4, and 88.1% of the metagenomic sequence reads were recruited by the concatenated genome sequences, respectively (Table 2). The Lactobacillus genomes recruited approximately 25% of all reads in the four samples. As they recruited reads throughout their lengths and identities close to 100%, Lactobacillus harbinensis, L. hilgardii, L. nagelii, and L. paracasei were considered to be present (Figure 3). The spread of recruited reads with identities between 80 and 90% throughout the lengths of the Lactobacillus hordei and Lactobacillus mali genome sequences indicated the presence of at least one species in the samples that was very similar to L. hordei and L. mali (Figure 3). Accounting for almost all reads recruited by Bifidobacterium genomes, the B. aquikefiri genome recruited between 17.4 and 34.8% of all reads and was considered to be present (Figure 3). The spread of the recruited reads with identities ranging mainly from 70 to 85% throughout the genome sequences of all three known Oenococcus species, namely O. alcoholitolerans, O. kitaharae, and O. oeni, indicated the presence of a novel Oenococcus species that is further referred to as Candidatus Oenococcus aquikefiri (Figure 3). The Cellulosimicrobium genus was detected in relative abundances higher than 0.5% of all reads in sample G24 solely (Figure 3). Also, 0.02% of all sequence reads from sample G72 were recruited by this genus with high identities. Among the two fungal genera, only S. cerevisiae and D. bruxellensis were considered to be present, although large parts of the Saccharomyces pastorianus genome also recruited reads with up to 100% identity. As their genome sequences did not recruit highly identical reads throughout their lengths, L. casei, L. kefiranofaciens, Lactobacillus kefiri, Lactobacillus parakefiri, and species of Bacillus, Cellulomonas, Isoptericola, Pediococcus, and Sanguibacter were considered not to be present.
Table 2. Percentage of all reads of a given metagenomic sequence data set representing water kefir fermentation samples (L, liquor; G, grains) after 24 and 72 h of fermentation, as recruited by the genus mentioned.
Figure 3. Metagenomic recruitment plots of species or genera found in the water kefir fermentation samples. Each dot represents a bin of metagenomic sequence reads from the data set L24 (water kefir liquor sample after 24 h of fermentation), recruited by a reference genome. The data set L24 is representative for all taxa, except for Cellulosimicrobium spp., for which the recruitment of reads from data set G24 (water kefir grains sample after 24 h of fermentation) is shown. The x-axis represents the length of a reference genome, or multiple reference genomes in the case of Oenococcus spp. (Oenococcus alcoholitolerans, Oenococcus kitaharae, Oenococcus oeni) and Cellulosimicrobium spp. (Cellulosimicrobium aquatile, Cellulosimicrobium cellulans, and Cellulosimicrobium funkei). The y-axis represents the identity of metagenomic reads to reference sequences. The color represents the number of reads per bin, normalized to the number of reads in the largest bin.
Temporal Shifts of the Microbial Communities in the Water Kefir Liquor and on the Water Kefir Grains
Based on the percentage of metagenomic sequence reads assigned with at least one tool, some shifts in the microbial communities on genus level occurred in the water kefir liquor over time. The percentage of reads assigned to a genus changed most on the water kefir grains, in particular in the case of Bifidobacterium with an apparent increase from 17.8 to 25.4%, and in the case of Saccharomyces with an apparent decrease from 23.3 to 16.4%. The latter also decreased in the water kefir liquor (19.3–13.9%). The percentage of reads assigned to Lactobacillus and Oenococcus remained relatively stable. For the former genus, it was around 25% on the water kefir grains and 27% in water kefir liquor, whereas for the latter genus it was around 6% on the water kefir grains and 12% in the water kefir liquor. The percentage of reads assigned to Dekkera slightly increased in the water kefir liquor (from 3.9 to 5.4%) and decreased on the water kefir grains (from 2.4 to 1.7%).
Metagenomic Assembly and Functional Analysis
Metagenomic Sequence Read Assembly and Contig Binning
The metagenomic sequence reads of the four data sets, as well as the combined data set, were assembled into contigs to enable de novo gene prediction and annotation. The sizes of the assemblies ranged from 24 to 39 Mbp (Table 3). The contigs of the five assemblies were binned into up to 37 bins with CONCOCT. Based on the BWA-MEM mapping of the contigs to reference genomes, almost all bins were associated with at least one of the microbial species, leaving only three bins of up to 27 kbp that could not be associated with any of the species present. Based on CheckM, for most of the bacterial species, in at least one of the assemblies, a bin was found containing contigs that formed a metagenome-assembled genome with more than 50% completeness and less than 10% contamination. Most of the assembled and annotated 16S and 23S rRNA gene sequences were not part of the main bins of the respective bacteria, except in the case of Candidatus O. aquikefiri in the G24 assembly. Metagenomic bins assigned to Oenococcus were purified and corresponding rRNA gene-containing contigs were added to produce MAGs (Table 4), although no pure Candidatus O. aquikefiri bin was obtained from the L72 assembly. The best MAG according to the assembly statistics was that from the L24 assembly, which exhibited a completeness and contamination of 97.86 and 0.18%, respectively.
Table 3. Statistics of the assembly of the metagenomic sequence reads into contigs of four water kefir fermentation samples (L, liquor; G, grains) obtained after 24 and 72 h of fermentation.
Table 4. Assembly statistics of the metagenome-assembled genome (MAG) sequences of the novel Oenococcus species, referred to as Candidatus Oenococcus aquikefiri.
Invertase-encoding genes or their fragments were found on contigs assigned to S. cerevisiae and D. bruxellensis, likely causing the hydrolysis of sucrose into D-glucose and D-fructose during water kefir fermentation, releasing these two monosaccharides for consumption by all capable microorganisms. Genes encoding hexokinases, fructokinases, and the enzymes of the Embden–Meyerhof–Parnas pathway and those for the conversion of pyruvate into ethanol were attributed to both S. cerevisiae and D. bruxellensis, although some of those genes were not fully assembled in the latter species.
The bacterial members of the water kefir ecosystem of the present study were also able to split sucrose into glucose and fructose, as L. hordei/mali possessed three genes, B. aquikefiri and L. harbinensis two genes, and L. nagelii, L. paracasei, and Candidatus O. aquikefiri one gene coding for a β-fructofuranosidase/sucrose-6-phosphate hydrolase. Additional fragments of such a gene were associated with L. harbinensis. Sucrose phosphorylase-encoding genes were found on contigs attributed to B. aquikefiri, Candidatus O. aquikefiri, L. hilgardii, and fragments on L. harbinensis contigs.
Genes encoding a phosphocarrier protein HPr and an EI component of the phosphoenolpyruvate-dependent phosphotransferase system (PEP-PTS) were associated with B. aquikefiri and all LAB members of the water kefir ecosystem of the present study. However, a gene encoding a PTS EIIBCA component that was at least 75% similar to the sucrose-specific PTS EIIBCA components encoded by Pediococcus pentosaceus and Streptococcus mutans (Swissprot accession numbers P43470 and P12655, respectively) was found only in L. harbinensis, L. hordei/mali, and L. nagelii, all in the vicinity of a β-fructofuranosidase/sucrose-6-phosphate hydrolase-encoding gene. Genes encoding the mannose-class PTS components EIIAB, EIIC, and EIID, each at least 74% similar to those of L. casei (NCBI accession numbers AAY63962, AAY63963, and AAY63964, respectively) or O. oeni (NCBI accession numbers ABJ56421, ABJ56422, and ABJ56423, respectively), were attributed to L. harbinensis, L. hordei/mali, L. nagelii, L. paracasei, and Candidatus O. aquikefiri. Genes encoding proteins similar to a putative glucose uptake protein from Bifidobacterium animalis subsp. lactis (NCBI accession number ACS48020) were attributed to all LAB members of the water kefir ecosystem of the present study, but not to B. aquikefiri.
The heterofermentative pathway to metabolize carbohydrates and produce lactic acid, ethanol, and acetic acid were linked with L. hilgardii and Candidatus O. aquikefiri, both the hetero- and homofermentative pathways with L. hordei/mali, L. paracasei, and L. nagelii, and the bifidobacterial shunt with B. aquikefiri. The core fermentative pathway of L. harbinensis could not be fully assembled. Genes encoding mannitol dehydrogenases were found on contigs attributed to L. hilgardii, Candidatus O. aquikefiri, and B. aquikefiri, making the reduction of fructose to mannitol possible.
Contigs attributed to S. cerevisiae contained all necessary genes for glycerol biosynthesis. Genes encoding glycerol kinase and glycerol-3-phosphate dehydrogenase were found on contigs attributed to Lactobacillus species, possibly enabling the use of glycerol in their metabolism.
Fragments of genes encoding glucansucrases of glycoside hydrolase family GH70 were attributed to L. hordei/mali, L. hilgardii, L. nagelii, and Candidatus O. aquikefiri. The product of the gene attributed to L. hilgardii was 99% identical to the glucansucrase of another L. hilgardii strain isolated from water kefir (NCBI accession number CBJ19544.1), although the former lacked the N-terminal repeat-containing region of 218 amino acids found in the latter. The glucansucrase sequences encoded by gene fragments on contigs attributed to the other three species were between 60 and 70% similar to the glucansucrase from L. hilgardii.
Pyruvate, Citrate, and Malate Metabolism
Genes responsible for the conversion of pyruvate into acetoin were linked with Lactobacillus species and Candidatus O. aquikefiri, namely through the consecutive action of α-acetolactate synthase and α-acetolactate decarboxylase. Genes encoding diacetyl reductases were linked with B. aquikefiri, L. hilgardii, L. hordei/mali, L. nagelii, and Candidatus O. aquikefiri, enabling the conversion of diacetyl, obtained after spontaneous conversion of α-acetolactate, into acetoin. All Lactobacillus species, except for L. paracasei, had the genetic means of converting one of the acetoin stereoisomers into a stereoisomer of 2,3-butanediol. Alternatively, pyruvate dehydrogenase genes were linked with L. hilgardii, L. paracasei, Candidatus O. aquikefiri, and possibly L. nagelii, enabling the decarboxylation of pyruvate into acetyl-CoA. The latter species also possessed a pyruvate synthase gene, potentially enabling it a similar conversion but using oxidized ferredoxin as the electron acceptor. Genes encoding formate C-acetyltransferases were associated with B. aquikefiri, L. hordei/mali, L. nagelii, L. paracasei, and possibly L. harbinensis, as the gene was not fully assembled, contributing to formate production by the conversion of pyruvate into acetyl-CoA. Pyruvate oxidase genes were linked with B. aquikefiri, L. hilgardii, L. paracasei, Candidatus O. aquikefiri, and possibly L. harbinensis, as its gene was not fully assembled, enabling the conversion of pyruvate into acetyl-phosphate and the concomitant production of hydrogen peroxide and carbon dioxide. All LAB and B. aquikefiri possessed the acetate kinase and phosphate acetyltransferase genes necessary to convert acetyl-phosphate to either acetate or acetyl-CoA, respectively.
All LAB species present in the water kefir metagenomes possessed genes encoding malic or malolactic enzymes to convert malic acid into lactic acid, whether or not via oxaloacetate and pyruvate. The fumarate hydratase- and fumarate reductase-encoding genes were found in all LAB species, except for L. harbinensis and possibly L. hordei/mali, due to missing genes or genes not fully assembled, enabling the conversion of malic acid into fumarate, and further into succinate. Genes necessary for citrate lyase activity were attributed to L. nagelii, L. paracasei, and Candidatus O. aquikefiri, enabling these LAB species to metabolize citrate to oxaloacetate and acetate.
Amino Acid Biosynthesis
Biosynthetic pathways of between 11 and 19 amino acids were reconstructed for all members of the water kefir ecosystem of the present study, except for Candidatus O. aquikefiri and L. harbinensis, whose pathways were reconstructed for only seven and two amino acids, respectively (Figure 4). Therefore, L. harbinensis was excluded from the following amino acid biosynthesis assessment. Complete biosynthetic pathways for all amino acids, except for L-arginine, were found for S. cerevisiae. All other members of the water kefir ecosystem of the present study possessed the enzyme-encoding genes to produce L-alanine from either L-cysteine (all microbial species) or pyruvate (L. paracasei and S. cerevisiae), L-glutamine from L-glutamate, L-proline from L-glutamate, and L-glycine from L-serine. The biosynthetic pathways for L-tyrosine and L-leucine were not reconstructed fully for any of the LAB species. Genes for chorismate biosynthesis were found in L. hilgardii and L. nagelii, but a transaldolase gene was not found in any of the LAB species. The transaldolase gene is necessary to produce the precursor erythrose 4-phosphate through the pentose phosphate pathway. In contrast, genes for the full biosynthetic pathways of aromatic amino acids were found for S. cerevisiae and B. aquikefiri, although the 3-phosphoshikimate 1-carboxyvinyltransferase gene of the latter was not found in full length. A nonsense mutation in the aspartate kinase gene of B. aquikefiri made it incapable of L-threonine, L-methionine, and L-lysine biosynthesis.
Figure 4. Biosynthetic potential of microbial species found in water kefir to produce amino acids, as assessed through functional analysis of the metagenomes. Black – biosynthesis very unlikely (conclusive evidence of loss-of-function mutations in genes involved in their biosynthesis). Gray – biosynthesis unlikely (at least one gene involved in their biosynthesis not assigned). Yellow – biosynthesis likely (at least one gene involved in their biosynthesis not assembled fully). Green – biosynthesis very likely (all genes involved in their biosynthesis found).
Vitamin and Cofactor Biosynthesis
Full biosynthetic pathways for phosphopantothenate, tetrahydrofolate, thiamine pyrophosphate, and riboflavin by S. cerevisiae were found, whereas the biosynthetic pathways for NAD(P)H and pyridoxal 5-phosphate contained some incomplete genes. Due to presumably incomplete or absent biosynthetic pathways, as observed by the absence of several genes per pathway, the bacterial species would depend on the precursors pantothenate, thiamine, and folic acid to produce phosphopantothenate, thiamine pyrophosphate, or tetrahydrofolate, respectively, with the possible exception for tetrahydrofolate in the case of L. hilgardii. The capability of pyridoxal 5-phosphate production from glutamate and glyceraldehyde 3-phosphate was found for B. aquikefiri, L. hilgardii, L. paracasei, and Candidatus O. aquikefiri. The capability of NADH biosynthesis from aspartate was found for B. aquikefiri and L. hilgardii, whereas the other bacterial species would need nicotinate or β-nicotinate D-ribonucleotide as precursors.
Taxonomic Characterization of the Novel Oenococcus Species
The core genome of the Oenococcus strains in the present analysis consisted of 615 orthogroups containing between 633 (O. oeni IOEB_C23) and 680 (O. alcoholitolerans UFRJ-M7.2.18) coding sequences. Among the 142 orthogroups present solely in Candidatus O. aquikefiri, 128 encoded hypothetical proteins, whereas the rest included genes encoding an arsenate reductase, a 2-hydroxy-3-oxopropionate reductase, a 5,10-methylenetetrahydrofolate reductase, a 5-methyltetrahydropteroyltriglutamate–homocysteine methyltransferase, an α-xylosidase, a putative hydrolase, an ATP-dependent Clp protease ATP-binding subunit ClpE, a ribonuclease R, a putative glycosyltransferase, a putative ribosomal N-acetyltransferase, an amidophosphoribosyltransferase, a uric acid permease, a quaternary ammonium compound-resistance protein, a peptide chain release factor 3, and a sensor histidine kinase. Conversely, 17 orthogroups were represented in all Oenococcus genomes analyzed, except for Candidatus O. aquikefiri. A phylogenetic tree constructed from single-copy core amino acid sequences common to the three known Oenococcus species, Candidatus O. aquikefiri, and L. garvieae as an outgroup, showed that the Oenococcus species found in the water kefir branched between O. oeni and O. kitaharae, with the ANI with strains of those species between 70 and 75% (Figure 5). The assembled 16S rRNA gene sequences belonging to Candidatus O. aquikefiri were 97% identical to the O. oeni JCM 6125 and O. kitaharae NRIC 0645 16S rRNA gene sequences (NCBI accession numbers LC071842.1 and NR_041312.1, respectively).
Figure 5. A rooted phylogenetic tree based on single-copy core amino acid sequences from 21 strains of different Oenococcus species and Lactococcus garvieae as outgroup, accompanied by average nucleotide identities between the genomes of the same strains. The blue dot on the phylogenetic tree denotes the rooting point, the black dots denote nodes with a support value higher than 0.95, and the red dots denote nodes with a support value lower than 0.95.
The taxonomic analysis of four water kefir metagenomes derived from two time points during a water kefir fermentation process, both water kefir liquors and water kefir grains, revealed a rather restricted bacterial diversity and an even more restricted yeast diversity. This is in agreement with culture-dependent data reported before (Magalhães et al., 2010; Gulitz et al., 2011; Laureys and De Vuyst, 2014, 2017) and even for the same water kefir fermentation processes (water kefir C in Laureys and De Vuyst, 2017). The metagenomic analysis of both water kefir liquors and water kefir grains of the present study revealed Lactobacillus, Oenococcus, and Bifidobacterium as the major bacterial genera, and Saccharomyces and Dekkera as the major yeast genera.
Using combinations of several tools and databases to obtain an overview of genus-level microbial diversity seemed to be a good approach to assess the composition of a microbial ecosystem. For instance, relying solely on Kraken or MetaPhlAn2 would result in an underestimation or even complete exclusion of Dekkera. Indeed, only sequences of Dekkera custersiana (Brettanomyces custersianus) were included in the Kraken database and no Dekkera marker genes were present in the MetaPhlAn2 database, since only a limited number of Dekkera sequences are available in public databases. Whereas in general shotgun metagenomics allows obtaining a good insight into the taxonomic composition of an ecosystem at genus level, species-level results heavily depend on the taxonomic analysis tools used (Illeghems et al., 2012). To circumvent this, metagenomic recruitment plots can be used based on the genome sequences of type strains, if possible, as type strains are the basis of species descriptions and nomenclature according to the Bacteriological Code (1990 Revision; Lapage et al., 1992). Indeed, based solely on the alignment of metagenomic sequence reads to sequences in public databases, species not present in the samples could be mistakenly declared as present, due to read alignment to conserved genes or genomic segments horizontally transferred from another species (Gogarten et al., 2002). Based on all taxonomic analysis tools used, including metagenomic recruitment plots, the bacterial species B. aquikefiri, Candidatus O. aquikefiri, L. harbinensis, L. hilgardii, a possibly novel Lactobacillus species related to L. hordei and L. mali, L. paracasei, and the yeast species D. bruxellensis and S. cerevisiae were considered to be present in all four water kefir metagenomes for both water kefir liquors and water kefir grains. These results are in agreement with previous studies that have reported the presence of LAB, most commonly lactobacilli of the L. casei group (e.g., L. paracasei), the Lactobacillus salivarius group (e.g., L. nagelii, L. hordei, and L. mali), and the Lactobacillus buchneri group (e.g., L. hilgardii), and yeasts, most commonly S. cerevisiae, whereas other LAB species, bifidobacteria (e.g., B. aquikefiri), acetic acid bacteria (e.g., A. fabarum), and D. bruxellensis occur less frequently (Moinas et al., 1980; Pidoux, 1989; Galli et al., 1995; Franzetti et al., 1998; Magalhães et al., 2010; Waldherr et al., 2010; Gulitz et al., 2011, 2013; Miguel et al., 2011; Hsieh et al., 2012; Marsh et al., 2013; Laureys and De Vuyst, 2014, 2017; Zanirati et al., 2015; Laureys et al., 2016, 2018). Indeed, all Lactobacillus species found in the present study have also been found culture-dependently (L. paracasei, L. hilgardii, and L. harbinensis) or culture-independently (all of them) in the same water kefir (C) examined previously (Laureys and De Vuyst, 2017). Also, Cellulosimicrobium cellulans was detected in the metagenomes derived from water kefir grains but not in those from water kefir liquor.
Species of the L. casei group commonly occur in water kefir (Pidoux, 1989; Galli et al., 1995; Magalhães et al., 2010; Gulitz et al., 2011, 2013; Miguel et al., 2011; Zanirati et al., 2015), although nomenclature of these species is problematic due to difficulties in distinguishing them phenotypically and even by 16S rRNA gene sequencing (Wuyts et al., 2017). As such, most strains designated as L. casei or L. paracasei are members of one species that is currently called L. paracasei (Judicial Commission of the International Committee on Systematics of Bacteria, 2008; Smokvina et al., 2013; Wuyts et al., 2017). The water kefir ecosystem of the present study contained L. paracasei, not L. casei, as shown through metagenomic recruitment plotting. L. hilgardii, a species frequently reported in water kefirs, and L. nagelii were first isolated from wines (Douglas and Cruess, 1936; Pidoux, 1989; Edwards et al., 2000; Waldherr et al., 2010; Gulitz et al., 2011, 2013; Laureys and De Vuyst, 2014, 2017; Zanirati et al., 2015). A Lactobacillus species closely related to L. hordei and L. mali, currently without a known genome sequence, was present in the water kefir samples of the present study. As L. mali and L. hordei are commonly found in water kefirs (Hsieh et al., 2012; Gulitz et al., 2013; Laureys and De Vuyst, 2014, 2017; Zanirati et al., 2015) and were first isolated from cider and malted barley, respectively (Carr and Davies, 1970; Rouse et al., 2008), it is not surprising that a similar species would be found in water kefir. So far, L. harbinensis, a species that was first isolated from traditional fermented vegetables, has only been reported in Belgian water kefirs (Miyamoto et al., 2005; Laureys and De Vuyst, 2014, 2017).
Bifidobacterium aquikefiri is a recently described bifidobacterial species isolated from a water kefir having a common ancestor with the water kefir studied here (Laureys et al., 2016). According to the 16S rRNA gene sequences available in public databases, B. aquikefiri was previously detected culture-independently through amplicon sequencing of water kefir grains (Gulitz et al., 2013). Also, B. psychraerophilum, a relative of B. aquikefiri, has previously been associated with water kefir (Hsieh et al., 2012; Gulitz et al., 2013; Laureys and De Vuyst, 2014). Bifidobacteriaceae have also been previously detected in yet another water kefir, albeit on family level only (Marsh et al., 2013).
The yeast species S. cerevisiae and D. bruxellensis are frequently reported in water kefir, the former being widespread and the latter occurring less frequently (Moinas et al., 1980; Galli et al., 1995; Franzetti et al., 1998; Magalhães et al., 2010; Gulitz et al., 2011; Miguel et al., 2011; Hsieh et al., 2012; Marsh et al., 2013; Laureys and De Vuyst, 2014, 2017). The assignment of metagenomic sequence reads to S. pastorianus was not surprising, as this yeast species is an interspecies hybrid between S. cerevisiae and Saccharomyces eubayanus (Libkind et al., 2011). Both yeast species found in water kefir appear also in other fermented (alcoholic) beverages, such as wines and ales, whereby S. cerevisiae is the main fermentative microorganism. In wine, D. bruxellensis is considered as a spoilage microorganism, although it is highly functional and desired in lambic beer (De Roos and De Vuyst, 2018; De Roos et al., 2018).
Although long-term stability of water kefir is assumed, the most prominent temporal changes were the decrease in relative abundance of S. cerevisiae and an increase in relative abundance of B. aquikefiri. This might be due to a fast growth of S. cerevisiae in a carbohydrate-rich environment, which are the conditions at the beginning of water kefir fermentations. It might also be that B. aquikefiri prefers growth conditions that are only met later in the water kefir fermentation, for example the presence of metabolic by-products of other microorganisms in the ecosystem. Alternatively, B. aquikefiri might grow better than other microorganisms in this water kefir ecosystem under certain limiting conditions. For instance, the potential of B. aquikefiri to produce tryptophan could give it an advantage upon tryptophan depletion, as tryptophan is the least abundant amino acid in figs (USDA, 2018).
The presence of Cellulosimicrobium species on the water kefir grains has not been reported in water kefir before, although ingredients such as figs contain cellulose. However, the enzyme preparations lyticase and Zymolyase used in the DNA extraction protocol are derived from cultures of Arthrobacter luteus. That name, along with Cellulomonas cellulans and Oerskovia xanthineolytica, is one of the older names of C. cellulans (Schumann and Stackebrandt, 2012). Hence, Cellulosimicrobium can be traced back to the materials used for DNA extraction. Indeed, DNA extraction kits and other laboratory reagents have previously been reported as a source of contaminating DNA (Salter et al., 2014). Moreover, due to the pooling of multiple aliquots during the DNA extraction in the case of the water kefir grains, the contribution of contaminating DNA from the enzymes was higher in the water kefir grain samples than in the water kefir liquor samples.
Apart from the water kefir grains, which serve as the inoculum, the major substrate inputs into the water kefir ecosystem were sucrose and dried figs. The sucrose added, as well as the glucose and fructose from the dried figs, were the main carbon and energy sources for microbial growth. Assuming the capability of transport, all microbial species present were predicted to metabolize sucrose, resulting in the production of lactic acid, acetic acid, ethanol, carbon dioxide, and exopolysaccharides, whose production has been shown experimentally in the water kefir under study previously (Laureys and De Vuyst, 2017). Mannitol production by reduction of fructose, frequently carried out by heterofermentative LAB species (Gänzle, 2015) and linked to L. hilgardii, Candidatus O. aquikefiri, and B. aquikefiri, and glycerol production associated with S. cerevisiae have also been shown experimentally in the water kefir under study previously (Laureys and De Vuyst, 2017). Based on the presence of a specific tryptophan residue, the glycoside hydrolase family GH70 proteins were likely classical sucrases as opposed to glucanotransferases (Brison et al., 2012; Gangoiti et al., 2017). Thus, a dextran could likely be produced from sucrose by L. hilgardii, which is known to be a species involved in water kefir grain formation (Brison et al., 2012; Gangoiti et al., 2017), and also by L. hordei/mali, L. nagelii, and Candidatus O. aquikefiri. Whether or not the latter three or other LAB species make major contributions to water kefir grain formation and growth is not known so far. However, recent studies on the structure and function of polysaccharides in water kefir grains and liquor have shown that dextrans with varying branching are present and are likely produced by different LAB species, whereby a dextran produced by L. hordei induces aggregation of S. cerevisiae, possibly making them more likely to attach to water kefir grains in vivo (Fels et al., 2018; Xu et al., 2018).
The figs were the main source of macro- and micronutrients, including nitrogen (amino acids from the protein fraction), sulfur, phosphorus, vitamins, and minerals, besides sucrose. Differences in the amino acid biosynthesis capabilities between microbial species may make them depend on each other to supply limiting amino acids, such as tryptophan, tyrosine, methionine, and histidine, by the conversion from other amino acids that are more plentiful in the figs, such as glutamate and aspartate (USDA, 2018). For example, with the possible exception of L. hilgardii in the case of all three aromatic amino acids and L. nagelii in the case of L-tyrosine, none of the LAB species reported were predicted to synthesize aromatic amino acids, which could be supplied by the dried figs or, when those were depleted, by S. cerevisiae or possibly B. aquikefiri. Similarly, LAB species likely depended on the figs or S. cerevisiae for their vitamin and cofactor requirements. Indeed, it has previously been reported that L. hordei and L. nagelii benefit from amino acids and pyridoxal phosphate released by the yeast Zygotorulaspora florentina (Stadie et al., 2013).
Given the high completeness and low contamination, the metagenome-assembled genome of Candidatus O. aquikefiri obtained in the present study would fulfill the criteria for a high-quality draft according to the genome reporting standard MIMAG, if all rRNA genes were present (Bowers et al., 2017). However, the presence of Lactobacillaceae interfered with the assembly or binning of rRNA genes. The phylogenetic tree, the ANI values, and the assembled 16S rRNA gene sequences all supported the novelty of the species found. To our knowledge, there is only one other report of an Oenococcus species in water kefir, namely O. oeni and O. kitaharae, which were identified based on 16S rRNA gene sequencing and amplified ribosomal DNA restriction analysis, respectively (Zanirati et al., 2015). Whereas O. oeni is typically found in wine (Sternes and Borneman, 2016), O. kitaharae and O. alcoholitolerans were isolated from a composting distilled shochu residue, and cachaça and ethanol fermentation processes, respectively (Endo and Okada, 2006; Badotti et al., 2014). The Oenococcus species found in the metagenomes of the present study was different from all three species mentioned above. As no Oenococcus isolates were retrieved in culture-dependent studies of this water kefir previously (Laureys and De Vuyst, 2017), the further description of the Candidatus O. aquikefiri species still needs to be performed after successful isolation.
The water kefir ecosystem of the present study contained B. aquikefiri, L. harbinensis, L. hilgardii, L. nagelii, L. paracasei, D. bruxellensis, and S. cerevisiae. Notable temporal shifts occurred only in relation to S. cerevisiae and B. aquikefiri, indicating their preference for growth earlier and later in the fermentation, respectively. This study further showed that a possibly novel Lactobacillus species related to L. hordei and L. mali as well as a novel Oenococcus sp., Candidatus O. aquikefiri, were present. Whereas the sequencing data were not conclusive for the novelty of the former LAB species, the novelty of the latter was well supported by the metagenomic sequence analyses. The functional analysis of the metagenomic data linked the production of metabolites during water kefir fermentation to certain species or group(s) of species. It showed that the microbial species present in the water kefir ecosystem studied had different capabilities of amino acid, vitamin, and cofactor biosynthesis, indicating possible cross-feeding interactions and the necessity of using dried figs (or other fruits) as a provider of macro- and micronutrients.
The datasets generated for this study can be found in European Nucleotide Archive, PRJEB21603 (metagenomic data sets) and PRJEB29525 (metagenome-assembled genome).
MV performed the metagenomic sequencing and subsequent bioinformatics analyses. SW and LDV designed and coordinated the study. MV, SW, and LDV wrote the manuscript. All authors read and approved the final manuscript.
We acknowledge financial support from the Research Council of the Vrije Universiteit Brussel (OZR234, SRP7, and IOF342 projects). Part of the computational resources and services was provided by the Shared ICT Services Centre funded by the Vrije Universiteit Brussel, the Flemish Supercomputer Center (VSC), and the Research Foundation Flanders (FWO). MV is the recipient of a Ph.D. fellowship of the FWO (grant numbers 1119916N and 1111918N).
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
David Laureys is acknowledged for providing the water kefir samples necessary to perform this study.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2019.00479/full#supplementary-material
Alneberg, J., Bjarnason, B. S., de Bruijn, I., Schirmer, M., Quick, J., Ijaz, U. Z., et al. (2014). Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146. doi: 10.1038/NMETH.3103
Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data. Available at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Badotti, F., Moreira, A. P. B., Tonon, L. A. C., de Lucena, B. T. L., Gomes, F. D. O., Kruger, R., et al. (2014). Oenococcus alcoholitolerans sp. nov., a lactic acid bacteria isolated from cachaça and ethanol fermentation process. Antonie van Leeuwenhoek. 106, 1259–1267. doi: 10.1007/s10482-014-0296-z
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., et al. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 4, 914–919. doi: 10.1111/2041-210X.12073
Bora, S. S., Keot, J., Das, S., Sarma, K., and Barooah, M. (2016). Metagenomics analysis of microbial communities associated with a traditional rice wine starter culture (Xaj-pitha) of Assam, India. 3 Biotech. 6:153. doi: 10.1007/s13205-016-0471-1
Bowers, R. M., Kyrpides, N. C., Stepanauskas, R., Harmon-Smith, M., Doud, D., Reddy, T. B. K., et al. (2017). Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731. doi: 10.1038/nbt.3893
Brison, Y., Pijning, T., Malbert, Y., Fabre,É, Mourey, L., Morel, S., et al. (2012). Functional and structural characterization of α-(1-2) branching sucrase derived from DSR-E glucansucrase. J. Biol. Chem. 287, 7915–7924. doi: 10.1074/jbc.M111.305078
Capella-Gutierrez, S., Silla-Martinez, J. M., and Gabaldon, T. (2009). TrimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973. doi: 10.1093/bioinformatics/btp348
De Filippo, C., Ramazzotti, M., Fontana, P., and Cavalieri, D. (2012). Bioinformatic approaches for functional annotation and pathway inference in metagenomics data. Brief. Bioinform. 13, 696–710. doi: 10.1093/bib/bbs070
De Roos, J., Vandamme, P., and De Vuyst, L. (2018). Wort substrate consumption and metabolite production during lambic beer fermentation and maturation explain the successive growth of specific bacterial and yeast species. Front. Microbiol. 9:2763. doi: 10.3389/fmicb.2018.02763
Dugat-Bony, E., Straub, C., Teissandier, A., Onésime, D., Loux, V., Monnet, C., et al. (2015). Overview of a surface-ripened cheese community functioning by meta-omics analyses. PLoS One 10:e0124360. doi: 10.1371/journal.pone.0124360
Duru, I. C., Laine, P., Andreevskaya, M., Paulin, L., Kananen, S., Tynkkynen, S., et al. (2018). Metagenomic and metatranscriptomic analysis of the microbial community in Swiss-type Maasdam cheese during ripening. Int. J. Food Microbiol. 281, 10–22. doi: 10.1016/j.ijfoodmicro.2018.05.017
Edwards, C. G., Collins, M. D., Lawson, P. A., and Rodriguez, A. V. (2000). Lactobacillus nagelii sp. nov., an organism isolated from a partially fermented wine. Int. J. Syst. Evol. Microbiol. 50, 699–702. doi: 10.1099/00207713-50-2-699
Emms, D. M., and Kelly, S. (2015). OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 16:157. doi: 10.1186/s13059-015-0721-2
Endo, A., and Okada, S. (2006). Oenococcus kitaharae sp. nov., a non-acidophilic and non-malolactic-fermenting Oenococcus isolated from a composting distilled shochu residue. Int. J. Syst. Evol. Microbiol. 56, 2345–2348. doi: 10.1099/ijs.0.64288-0
Escobar-Zepeda, A., Sanchez-Flores, A., and Quirasco Baruch, M. (2016). Metagenomic analysis of a Mexican ripened cheese reveals a unique complex microbiota. Food Microbiol. 57, 116–127. doi: 10.1016/j.fm.2016.02.004
Ferrocino, I., Bellio, A., Giordano, M., Macori, G., Romano, A., Rantsiou, K., et al. (2018). Shotgun metagenomics and volatilome profile of the microbiota of fermented sausages. Appl. Environ. Microbiol. 84, e02120-17. doi: 10.1128/AEM.02120-17
Gangoiti, J., van Leeuwen, S. S., Gerwig, G. J., Duboux, S., Vafiadi, C., Pijning, T., et al. (2017). 4,3-α-glucanotransferase, a novel reaction specificity in glycoside hydrolase family 70 and clan GH-H. Sci. Rep. 7:39761. doi: 10.1038/srep39761
Gulitz, A., Stadie, J., Ehrmann, M. A., Ludwig, W., and Vogel, R. F. (2013). Comparative phylobiomic analysis of the bacterial community of water kefir by 16S rRNA gene amplicon sequencing and ARDRA analysis. J. Appl. Microbiol. 114, 1082–1091. doi: 10.1111/jam.12124
Hsieh, H.-H., Wang, S.-Y., Chen, T.-L., Huang, Y.-L., and Chen, M.-J. (2012). Effects of cow’s and goat’s milk as fermentation media on the microbial ecology of sugary kefir grains. Int. J. Food Microbiol. 157, 73–81. doi: 10.1016/j.ijfoodmicro.2012.04.014
Illeghems, K., De Vuyst, L., Papalexandratou, Z., and Weckx, S. (2012). Phylogenetic analysis of a spontaneous cocoa bean fermentation metagenome reveals new insights into its bacterial and fungal community diversity. PLoS One 7:e34080. doi: 10.1371/journal.pone.0038040
Illeghems, K., Weckx, S., and De Vuyst, L. (2015). Applying meta-pathway analyses through metagenomics to identify the functional properties of the major bacterial communities of a single spontaneous cocoa bean fermentation process sample. Food Microbiol. 50, 54–63. doi: 10.1016/j.fm.2015.03.005
Judicial Commission of the International Committee on Systematics of Bacteria (2008). The type strain of Lactobacillus casei is ATCC 393, ATCC 334 cannot serve as the type because it represents a different taxon, the name Lactobacillus paracasei and its subspecies names are not rejected and the revival of the name ‘Lactobacillus zeae’ contravenes Rules 51b (1) and (2) of the International Code of Nomenclature of Bacteria. Opinion 82. Int. J. Syst. Evol. Microbiol. 58, 1764–1765. doi: 10.1099/ijs.0.2008/005330-0
Jung, J. Y., Lee, S. H., Kim, J. M., Park, M. S., Bae, J. W., Hahn, Y., et al. (2011). Metagenomic analysis of kimchi, a traditional Korean fermented food. Appl. Environ. Microbiol. 77, 2264–2274. doi: 10.1128/AEM.02157-10
Lapage, S. P., Sneath, P. H. A., Lessel, E. F., Skerman, V. B. D., Seeliger, H. P. R., and Clark, W. A. (eds) (1992). International Code of Nomenclature of Bacteria: Bacteriological Code (1990 Revision). Washington, DC: ASM Press.
Laureys, D., Cnockaert, M., De Vuyst, L., and Vandamme, P. (2016). Bifidobacterium aquikefiri sp. nov., isolated from water kefir. Int. J. Syst. Evol. Microbiol. 66, 1281–1286. doi: 10.1099/ijsem.0.000877
Laureys, D., and De Vuyst, L. (2014). Microbial species diversity, community dynamics, and metabolite kinetics of water kefir fermentation. Appl. Environ. Microbiol. 80, 2564–2572. doi: 10.1128/AEM.03978-13
Laureys, D., and De Vuyst, L. (2017). The water kefir grain inoculum determines the characteristics of the resulting water kefir fermentation process. J. Appl. Microbiol. 122, 719–732. doi: 10.1111/jam.13370
Lee, H. K., Yi, H., and Chun, J. (2011). rRNASelector: a computer program for selecting ribosomal RNA encoding sequences from metagenomic and metatranscriptomics shotgun libraries. J. Microbiol. 49, 689–691. doi: 10.1007/s12275-011-1213-z
Li, D. H., Liu, C. M., Luo, R. B., Sadakane, K., and Lam, T. W. (2015). MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676. doi: 10.1093/bioinformatics/btv033
Li, Z., Feng, C., Luo, X., Yao, H., Zhang, D., and Zhang, T. (2018). Revealing the influence of microbiota on the quality of Pu-erh tea during fermentation process by shotgun metagenomic and metabolomic analysis. Food Microbiol. 76, 405–415. doi: 10.1016/j.fm.2018.07.001
Libkind, D., Hittinger, C. T., Valèrio, E., Gonçalves, C., Dover, J., Johnston, M., et al. (2011). Microbe domestication and the identification of the wild genetic stock of lager-brewing yeast. Proc. Natl. Acad. Sci. U.S.A. 108, 14539–14544. doi: 10.1073/pnas.1105430108
Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M., and Henrissat, B. (2014). The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495. doi: 10.1093/nar/gkt1178
Magalhães, K. T., de Pereira, G. V., Dias, D. R., and Schwan, R. F. (2010). Microbial communities and chemical changes during fermentation of sugary Brazilian kefir. World J. Microbiol. Biotechnol. 26, 1241–1250. doi: 10.1007/s11274-009-0294-x
Marsh, A. J., O’Sullivan, O., Hill, C., Ross, R. P., and Cotter, P. D. (2013). Sequence-based analysis of the microbial composition of water kefir from multiple sources. FEMS Microbiol. Lett. 348, 79–85. doi: 10.1111/1574-6968.12248
McMurdie, P. J., and The Biom-Format Team (2014). Biom: An Interface Package (Beta) for the BIOM File Format. R Package Version 0.3.12. Available at: https://CRAN.R-project.org/src/contrib/Archive/biom
Miguel, M. G., Cardoso, P. G., Magalhães, K. T., and Schwan, R. F. (2011). Profile of microbial communities present in tibico (sugary kefir) grains from different Brazilian states. World J. Microbiol. Biotechnol. 27, 1875–1884. doi: 10.1007/s11274-010-0646-6
Miyamoto, M., Seto, Y., Hao, D. H., Teshima, T., Sun, Y. B., Kabuki, T., et al. (2005). Lactobacillus harbinensis sp. nov., consisted of strains isolated from traditional fermented vegetables ‘Suan cai’ in Harbin, Northeastern China and Lactobacillus perolens DSM 12745. Syst. Appl. Microbiol. 28, 688–694. doi: 10.1016/j.syapam.2005.04.001
Moinas, M., Horisberger, M., and Bauer, H. (1980). The structural organization of the Tibi grain as revealed by light, scanning and transmission microscopy. Arch. Microbiol. 128, 157–161. doi: 10.1007/BF00406153
Nalbantoglu, U., Cakar, A., Dogan, H., Abaci, N., Ustek, D., Sayood, K., et al. (2014). Metagenomic analysis of the microbial community in kefir grains. Food Microbiol. 41, 42–51. doi: 10.1016/j.fm.2014.01.014
Neuwirth, E. (2014). RColorBrewer: ColorBrewer Palletes. R Package Version 1.1-2. Available at: https://CRAN.R-project.org/package=RColorBrewer
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., and Tyson, G. W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055. doi: 10.1101/gr.186072.114
Pidoux, M. (1989). The microbial flora of sugary kefir grain (the gingerbeer plant): biosynthesis of the grains from Lactobacillus hilgardii producing a polysaccharide gel. J. Appl. Microbiol. 5, 223–238. doi: 10.1007/BF01741847
Prado, M. R., Blandón, L. M., Vandenberghe, L. P. S., Rodrigues, C., Castro, G. R., Thomaz-Soccol, V., et al. (2015). Milk kefir: composition, microbial cultures, biological activities, and related products. Front. Microbiol. 6:1177. doi: 10.3389/fmicb.2015.01177
Ranjan, R., Rani, A., Metwally, A., McGee, H. S., and Perkins, D. L. (2016). Analysis of the microbiome: advantages of whole genome shotgun sequencing versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 4, 967–977. doi: 10.1016/j.bbrc.2015.12.083
Rouse, S., Canchaya, C., and van Sinderen, D. (2008). Lactobacillus hordei sp. nov., a bacteriocinogenic strain isolated from malted barley. Int. J. Syst. Evol. Microbiol. 58, 2013–2017. doi: 10.1099/ijs.0.65584-0
Salter, S. J., Cox, M. J., Turek, E. M., Calus, S. T., Cookson, W. O., Moffatt, M. F., et al. (2014). Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12:87. doi: 10.1186/s12915-014-0087-z
Salvetti, E., Campanaro, S., Campedelli, I., Fracchetti, F., Gobbi, A., Tornielli, G. B., et al. (2016). Whole-metagenome-sequencing-based community profiles of Vitis vinifera L. cv. Corvina berries withered in two post-harvest conditions. Front. Microbiol. 7:937. doi: 10.3389/fmicb.2016.00937
Schumann, P., and Stackebrandt, E. (2012). “Genus II. Cellulosimicrobium,” in Bergey’s Manual of Systematic Bacteriology: The Actinobacteria, Vol. 5, eds W. Whitman, M. Goodfellow, P. Kämpfer, H.-J. Busse, M. Trujillo, W. Ludwig, et al. (New York, NY: Springer-Verlag), 1002–1006.
Smokvina, T., Wels, M., Polka, J., Chervaux, C., Brisse, S., and Boekhorst, J. (2013). Lactobacillus paracasei comparative genomics: towards species pan-genome definition and exploitation of diversity. PLoS One 8:e68371. doi: 10.1371/journal.pone.0068731
Smukowski Heil, C., Burton, J. N., Liachko, I., Friedrich, A., Hanson, N. A., Morris, C. L., et al. (2018). Identification of a novel interspecific hybrid yeast from a metagenomic spontaneously inoculated beer sample using Hi-C. Yeast 35, 71–84. doi: 10.1002/yea.3280
Stadie, J., Gulitz, A., Ehrmann, M. A., and Vogel, R. F. (2013). Metabolic activity and symbiotic interactions of lactic acid bacteria and yeasts isolated from water kefir. Food Microbiol. 35, 92–98. doi: 10.1016/j.fm.2013.03.009
Sternes, P. R., Lee, D., Kutyna, D. R., and Borneman, A. R. (2017). A combined meta-barcoding and shotgun metagenomic analysis of spontaneous wine fermentation. Gigascience 6, 1–30. doi: 10.1093/gigascience/gix040
Tatusova, T., Ciufo, S., Fedorov, B., O’Neill, K., and Tolstoy, I. (2014). RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 42, D553–D559. doi: 10.1093/nar/gkt1274
Truong, D. T., Franzosa, E. A., Tickle, T. L., Scholz, M., Weingart, G., Pasolli, E., et al. (2015). MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903. doi: 10.1038/nmeth.3589
USDA (2018). National Nutrient Database for Standard Reference Legacy Release. Available at: https://ndb.nal.usda.gov/ndb/
Vermote, L., Verce, M., De Vuyst, L., and Weckx, S. (2018). Amplicon and shotgun metagenomic sequencing indicates that microbial ecosystems present in cheese brines reflect environmental inoculation during the cheese production process. Int. Dairy. J. 87, 44–53. doi: 10.1016/j.idairyj.2018.07.010
Waldherr, F. W., Doll, V. M., Meißner, D., and Vogel, R. F. (2010). Identification and characterization of a glucan-producing enzyme from Lactobacillus hilgardii TMW 1.828 involved in granule formation of water kefir. Food Microbiol. 27, 672–678. doi: 10.1016/j.fm.2010.03.013
Walsh, A. M., Crispie, F., Daari, K., O’Sullivan, O., Martin, J. C., Arthur, C. T., et al. (2017). Strain-level metagenomic analysis of the fermented beverage nunu highlights the potential food safety risks. Appl. Environ. Microbiol. 83:e1144-17. doi: 10.1128/AEM.01144-17
Walsh, A. M., Crispie, F., Kilcawley, K., O’Sullivan, O., O’Sullivan, M. G., Claesson, M. J., et al. (2016). Microbial succession and flavor production in the fermented dairy beverage kefir. mSystems 1:e00052-16. doi: 10.1128/mSystems.00052-16
Walsh, A. M., Crispie, F., O’Sullivan, O., Finnegan, L., Claesson, M. J., and Cotter, P. D. (2018). Species classifier choice is a key consideration when analyzing low-complexity food microbiome data. Microbiome 6:50. doi: 10.1186/s40168-018-0437-0
Wang, Q., Garrity, G. M., Tiedje, J. M., and Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. doi: 10.1128/AEM.00062-07
Wickham, H. (2016). Scales: Scale Functions for Visualization. R Package Version 0.4.0. Available at: https://CRAN.R-project.org/package=scales
Wolfe, B. E., Button, J. E., Santarelli, M., and Dutton, R. J. (2014). Cheese rind communities provide tractable systems for in situ and in vitro studies of microbial diversity. Cell 158, 422–433. doi: 10.1016/j.cell.2014.05.041
Wu, L.-H., Lu, Z.-M., Zhang, X.-J., Wang, Z.-M., Yu, Y.-J., Shi, J.-S., et al. (2017). Metagenomics reveals flavour metabolic network of cereal vinegar microbiota. Food Microbiol. 62, 23–31. doi: 10.1016/j.fm.2016.09.010
Wuyts, S., Wittouck, S., De Boeck, I., Allonsius, C. N., Pasolli, E., Segata, N., et al. (2017). Large-scale phylogenomics of the Lactobacillus casei group highlights taxonomic inconsistencies and reveals novel clade-associated features. mSystems 2:e00061-17. doi: 10.1128/mSystems.00061-17
Xu, D., Fels, L., Wefers, D., Behr, J., Jakob, F., and Voger, R. F. (2018). Lactobacillus hordei dextrans induce Saccharomyces cerevisiae aggregation and network formation on hydrophilic surfaces. Int. J. Biol. Macromol. 115, 236–242. doi: 10.1016/j.ijbiomac.2018.04.068
Yoon, S. H., Ha, S. M., Lim, J. M., Kwon, S. J., and Chun, J. (2017). A large-scale evaluation of algorithms to calculate average nucleotide identity. Antonie Van Leeuwenhoek 110, 1281–1286. doi: 10.1007/s10482-017-0844-4
Yu, G., Smith, D., Zhu, H., Guan, Y., and Lam, T. T. (2017). ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36. doi: 10.1111/2041-210X.12628
Zanirati, D. F., Abatemarco, M. Jr., de Cicco Sandes, S. H., Nicoli, J. R., Cantini Nunes, A., and Neumann, E. (2015). Selection of lactic acid bacteria from Brazilian kefir grains for potential use as starter or probiotic cultures. Anaerobe 32, 70–76. doi: 10.1016/j.anaerobe.2014.12.007
Keywords: shotgun metagenomics, water kefir, microbial diversity, functional potential, Oenococcus
Citation: Verce M, De Vuyst L and Weckx S (2019) Shotgun Metagenomics of a Water Kefir Fermentation Ecosystem Reveals a Novel Oenococcus Species. Front. Microbiol. 10:479. doi: 10.3389/fmicb.2019.00479
Received: 22 November 2018; Accepted: 25 February 2019;
Published: 13 March 2019.
Edited by:Fabio Minervini, University of Bari Aldo Moro, Italy
Reviewed by:Cristiana Garofalo, Marche Polytechnic University, Italy
Matthias Ehrmann, Technische Universität München, Germany
Copyright © 2019 Verce, De Vuyst and Weckx. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Stefan Weckx, email@example.com