A Vegetable Fermentation Facility Hosts Distinct MicrobiomesReflecting the Production Environment

Jonah E. Einson,a,b Asha Rani,a Xiaomeng You,a Allison A. Rodriguez,c Clifton L. Randell,c Tammy Barnaba,d

Mark K. Mammel,d Michael L. Kotewicz,d Christopher A. Elkins,d* David A. Selaa,e

aDepartment of Food Science, University of Massachusetts Amherst, Amherst, Massachusetts, USAbCommonwealth Honors College, University of Massachusetts Amherst, Amherst, Massachusetts, USAcFood and Drug Administration, Winchester Engineering Analytical Center, Winchester, Massachusetts, USAdDivision of Molecular Biology, Office of Applied Research and Safety Assessment, Center for Food Safety andApplied Nutrition, U.S. Food and Drug Administration, Laurel, Maryland, USA

eDepartment of Microbiology and Physiological Systems, University of Massachusetts Medical School,Worcester, Massachusetts, USA

ABSTRACT Fermented vegetables are highly popular internationally in part due totheir enhanced nutritional properties, cultural history, and desirable sensorial proper-ties. In some instances, fermented foods provide a rich source of the beneficial mi-crobial communities that could promote gastrointestinal health. The indigenous mi-crobiota that colonize fermentation facilities may impact food quality, food safety,and spoilage risks and maintain the nutritive value of the product. Here, micro-biomes within sauerkraut production facilities were profiled to characterize varianceacross surfaces and to determine the sources of these bacteria. Accordingly, we usedhigh-throughput sequencing of the 16S rRNA gene in combination with whole-genome shotgun analyses to explore biogeographical patterns of microbial diversityand assembly within the production facility. Our results indicate that raw cabbageand vegetable handling surfaces exhibit more similar microbiomes relative to thefermentation room, processing area, and dry storage surfaces. We identified bio-marker bacterial phyla and families that are likely to originate from the raw cabbageand vegetable handling surfaces. Raw cabbage was identified as the main source ofbacteria to seed the facility, with human handling contributing a minor source of in-oculation. Leuconostoc and Lactobacillaceae dominated all surfaces where spontane-ous fermentation occurs, as these taxa are associated with the process. Wall, floor,ceiling, and barrel surfaces host unique microbial signatures. This study demon-strates that diverse bacterial communities are widely distributed within the produc-tion facility and that these communities assemble nonrandomly, depending on thesurface type.

IMPORTANCE Fermented vegetables play a major role in global food systems andare widely consumed by various global cultures. In this study, we investigated an in-dustrial facility that produces spontaneous fermented sauerkraut without the aid ofstarter cultures. This provides a unique system to explore and track the origins of an“in-house” microbiome in an industrial environment. Raw vegetables and the sur-faces on which they are handled were identified as the likely source of bacterialcommunities rather than human contamination. As fermented vegetables increase inpopularity on a global scale, understanding their production environment may helpmaintain quality and safety goals.

KEYWORDS microbiome, lactic acid bacteria, fermentation, microbiology of the builtenvironment, food microbiology, phylogenetic diversity

Received 10 July 2018 Accepted 29 August2018

Accepted manuscript posted online 31August 2018

Citation Einson JE, Rani A, You X, Rodriguez AA,Randell CL, Barnaba T, Mammel MK, Kotewicz ML,Elkins CA, Sela DA. 2018. A vegetablefermentation facility hosts distinct microbiomesreflecting the production environment. ApplEnviron Microbiol 84:e01680-18. https://doi.org/10.1128/AEM.01680-18.

Editor Johanna Björkroth, University of Helsinki

Copyright © 2018 American Society forMicrobiology. All Rights Reserved.

Address correspondence to David A. Sela,davidsela@umass.edu.

* Present address: Christopher A. Elkins, Clinicaland Environmental Microbiology Branch,Division of Healthcare Quality Promotion,National Center for Emerging and ZoonoticInfectious Diseases, Centers for Disease Controland Prevention, Atlanta, Georgia, USA.

J.E.E. and A.R. contributed equally to this work.

FOOD MICROBIOLOGY

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Lactic acid bacteria (LAB) are naturally found on vegetable surfaces. As a result offermentative metabolism, they produce organic acids (1, 2). Lactic acid fermentation

could proceed by indigenous bacteria or with the aid of starter cultures to shift the bacterialpopulations inherent to vegetable microbiomes (3). Although LAB constitute a smallfraction of the total bacteria present on raw vegetables, they quickly adapt to conditionswhen subjected to a purposeful fermentation process. The LAB flourish, whereas otherbacteria that are incapable of withstanding low pH and high salinity are inactivated. Theseinclude potential spoilage and pathogenic microorganisms that are outcompeted by theLAB. This has lasting benefits to food production, including maintaining safety and stabilityprior to consumption. In terms of organoleptic properties, fermentations transform rawvegetables to provide distinctive flavors and textures dependent on the desired outcome(e.g., kimchi, sauerkraut, cucumber pickles, etc.). Fermented foods have become increas-ingly popular in recent years due to purported beneficial properties to enhance gastroin-testinal health and overall well-being (4–10).

In order to understand the microbiology of a vegetable fermentation facility, thebacterial communities colonizing the facility were profiled. This is an effort to link theestablishment of bacterial microbiomes with desired production outcomes, such asmitigating safety and spoilage risks and standardizing desirable traits for the consumer.

A limited number of food fermentation environments have been profiled previously.Bacterial and fungal communities were linked to the cheese ripening process in acheese production facility (11). In another study, microbial assemblages were charac-terized in a brewery environment (12). Interestingly, raw substrates, such as grain, hops,and yeast, were the main source of inoculation of the brewery environment, asopposed to human skin, outdoor air, and soil sources. Fermentative microbes aretypically considered deleterious contaminants unless purposely sought in the produc-tion of sour beer and other brewing practices. Both the creamery and brewery studiesidentified starter cultures as a major contributor to surface-colonizing microbiota. Thepotential for shotgun metagenomics in foods has been examined in cheese, kimchi,Chinese rice wine, and cocoa beans (13).

Standardization of natural sauerkraut fermentation may be hindered, as it relies onseveral factors with some more controllable than others. To date, the physical location(i.e., where barrels are housed) where fermentation occurs has not been linked to finalproduct quality in a scientifically rigorous manner. On occasion, isolated batches offermented vegetables spoil although they are located in the same fermentation roomas unspoiled batches. This occurs in the absence of a clear explanation that mayultimately be due to microbiological conditions. Briefly, the microbiologies of builtenvironments are typically vastly different depending on their primary purpose. Thefermented food production facility studies described herein use different substrates(i.e., dairy versus grain versus vegetables) and employ fermentation approaches thatinvolve a different set of microbes that are unique to the system. Moreover, the sponta-neous fermentation (i.e., no starter culture) of this sauerkraut production increases theuncertainty of the microbial composition of the facility, if not the food product itself. Thisprovided the key questions and scientific motivation to profile the microbiology of severalsurfaces that contact raw cabbage throughout sauerkraut production. The phylogeneticdiversity measures of the bacterial communities in the production facility were comparedto identify sources of microbial contamination using Bayesian modeling. Moreover, whole-genome shotgun sequencing was performed to provide further granularity to the micro-biology of the particular production facility. High-throughput 16S rRNA gene ampliconsequencing provides a deeper resolution of the unique ecosystems that colonize thefacility. The objective of the study is to provide a better understanding of the bacterialcommunities contaminating or inhabiting facilities where vegetables are fermented. Giventhe role of endogenous bacteria in traditional spontaneous vegetable fermentations, thisstudy serves as a model for human-microbe transfers in a food processing system thatrequires a considerable degree of human handling. Microbes could be transferred fromhuman skin to facility surfaces during human contact experienced in locations such as the

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processing and storage areas. These include bacterial families, such as Streptococcaceae andPropionibacteriaceae.

RESULTS AND DISCUSSIONProduction facility microbiota are distributed relative to sampling location.

Amplicon sequencing of the 16S rRNA phylogenetic marker gene was employed tosurvey bacterial consortia in the fermentation facility environment. In total, 32 swabsamples were collected representing storage areas, a processing room, vegetablehanding equipment, fermentation rooms, door handles, and a restroom. See Fig. 1 forthe detailed scheme of sampling locations within the facility and Fig. S14 in thesupplemental material for material flow to the sampling locations.

Beta diversity was measured to assess community differences between locations.Bray-Curtis dissimilarity of microbial communities at the phylum level revealed thatsurfaces generally cluster based on their physical location (Fig. 2). Accordingly, rawvegetable and vegetable handling surfaces are clustered closely with higher propor-tions of Proteobacteria, whereas environmental surfaces (i.e., processing room, fermen-tation room, and dry storage surfaces) exhibit an abundance of Firmicutes and Actino-bacteria (Fig. 2). Thus, distinctly clustered patterns emerged, indicating phylogeneticvariation between bacterial communities at different sampling sites. Two samples fromthe processing room floor (i.e., floor drain and mopping sink surfaces) exhibited higherconcentrations of Actinobacteria and clustered independently from the other collectedsamples. It has been previously reported that the Gram-negative Proteobacteria aremore abundant on moist surfaces, whereas Gram-positive Firmicutes are abundant ondry and cold residential kitchen surfaces (14). This is consistent with the detection ofFirmicutes in high abundance on refrigerator surfaces (Fig. 2). The phylogenetic profilesof the floors of the dry storage and fermentation room were more heterogeneous atthe phylum level, characterized by various proportions of Proteobacteria, Firmicutes, and

FIG 1 Facility map with sampling surface key. Facility map is color-coded to show different surface types, sample names, and sampling areas. Raw vegetablesenter the facility through a garage door on the left side of the map and are transported on wooden pallets to the processing room and hand-processed forfermentation.

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Actinobacteria. In general, the phyla Proteobacteria, Firmicutes, and Actinobacteria sig-nificantly varied between raw vegetables (i.e., raw cabbage and vegetable handlingsurfaces) and environmental surfaces (i.e., processing room, fermentation room, and drystorage surfaces) (Fig. 3).

At the family level, storage room floors were characterized by high relative abun-dances of Micrococcaceae, Lactobacillaceae, Leuconostocaceae, and Bacillaceae (Fig. 4).This is expected since Lactobacillaceae and Leuconostocaceae are the primary bacterialactors early in vegetable fermentations and other food microbiomes (2, 15–19). Simi-larly, high abundances of Leuconostocaceae were observed in the fermentation room,both on the floor and in filled barrels, and on most door handle surfaces (Fig. 4).Interestingly, this taxon was detected in very low abundance (�0.01%) in raw vegetableand food processing equipment, although the more sensitive whole-genome sequencedata indicated 1 to 2% abundance levels in swab samples from raw cabbage or sink butnot the shredder (Fig. S13). This suggests that appreciable concentrations of Leucono-stocaceae are introduced to the facility as a result of the fermentation process. This isconsistent with findings reported by Bokulich and Mills, in that substrate contact is thegreatest predictor of in-house microbiome composition (11). High abundances ofMoraxellaceae and Pseudomonadaceae were detected in raw vegetables and vegetablehandling areas, thus distinguishing them from other environmental surfaces. This isconsistent with the findings from a previous study that indicated a relatively higherabundance of these families on raw vegetables (20). Raw vegetable and vegetablehandling surfaces were significantly different in Moraxellaceae, Leuconostocaceae,Lactobacillales, and Pseudomonadaceae abundances compared to processing room and

FIG 2 Taxon abundance heatmap. Heatmap depicts the relative abundances of bacterial phyla across all surfaces. The surface type of thesample is indicated by the colored block on the top of the heatmap.

FIG 3 Box chart representation for phylum-level comparison. Relative abundances of bacterial phyla Proteobacteria (a), Firmicutes (b), and Actinobacteria (c) werecompared across raw vegetables and environmental surfaces in the facility. Surfaces are organized by similar locations within the facility. Raw vegetable groupincludes the raw cabbage and vegetable handling surfaces, whereas the environmental group includes the processing room, fermentation room, and drystorage surfaces. Statistical analysis was computed using an unpaired t test with Welch’s correction (*, P � 0.05; **, P � 0.01; ***, P � 0.001).

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dry storage areas (Fig. 5). Streptococcaceae and Propionibacteriaceae were significantlyhigher on fermentation barrels, which is likely due to increased human handlingcompared to that on other surfaces. Further details of bacterial families that signifi-cantly differed between surfaces are depicted in Fig. 5 and Table 1.

FIG 4 Relative abundances of bacterial families across surface types. Each colored bar represents a bacterialfamily percentage that comprises the microbiome sample. Surfaces are organized by similar location. HS, handsink; CB, cutting board; S, shredder; MB, mixing bin; MS, mop sink (see Fig. 1 for surface descriptions).

FIG 5 Mean relative abundances of the bacterial families according to surface swab classification. One-way analysis of variance (ANOVA) with posthoc Tukey test was performed, with significance at a P value of �0.05. a to m, significant differences in mean relative abundance for bacterialfamily between two surface swabs. For example, “a, b” on the top of the bar represents that the family Moraxellaceae is significantly differentbetween vegetable handling and dry storage surfaces, as well as between vegetable handling and processing room surfaces. a, vegetablehandling versus dry storage; b, vegetable handling versus processing room; c, raw vegetable versus processing room; d, vegetable handlingversus fermentation room; e, fermentation barrels versus fermentation room; f, fermentation barrels versus dry storage; g, processing room versusfermentation barrels; h, raw vegetable versus fermentation barrels; i, vegetable handling versus fermentation barrels; j, processing room versusdry storage; k, dry storage versus fermentation room; l, raw vegetable versus fermentation room; m, processing room versus fermentation room.“Other” includes collective abundance of all other bacterial families.

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Raw vegetable microbiomes segregated from all other environmental surfaces usingnonmetric multidimensional scaling (NMDS) (Fig. S2a). Analysis of similarity (ANOSIM)using Bray-Curtis distances indicates that these two groups differ significantly (R � 0.69,P � 0.05) from each other compared to within-group differences (Fig. S2b). Rawvegetables had significantly lower Shannon diversity index and species richness (P �

0.05) values than did environmental surfaces, despite similar species evenness in thetwo groups (Fig. S3). This suggests that diversity index and species richness vary due todistinct microbial community compositions rather than uneven species distribution.

Linear discriminant analysis effect size (LEfSe) analysis at the phylum level (Fig. S4a)identified Proteobacteria and Bacteroidetes as highly abundant in raw vegetables com-pared to Firmicutes, Actinobacteria, Chloroflexi, and several other phyla that are pre-dominant on facility surfaces. These phyla can also serve as biomarker phyla, asdepicted in an odds ratio plot (Fig. S4b). LEfSe analysis at the family level identifiedPseudomonadaceae, Enterobacteriaceae, and Comamonadaceae families as highly abun-dant on raw vegetables compared to Lactobacillaceae, Leuconostocaceae, and otherfamilies within the production environment (Fig. S5a). Log odds ratios identified theseas biomarker families that can be used to segregate these groups. Thus, the Pseu-domonadaceae, Enterobacteriaceae, and Comamonadaceae families are candidate bio-marker families for raw vegetables compared to Lactobacillaceae and Leuconostocaceaefor fermentation-associated biomarkers. A detailed cataloguing of the 33 abundantbacterial families identified is depicted in Fig. S5b.

Dispersal patterns of genera associated with spontaneous fermentation. Byvisually mapping all surface microbiomes to their physical locations within the produc-tion facility, a spatial model of bacterial niche colonization emerges (Fig. 6a). The mostprominent genus anchored to a specific physical location is Leuconostoc, members of

TABLE 1 Relative abundances of bacteria in different swab surfaces at family level

Family

Abundance by surface

Raw vegetableVegetablehandling

Processingroom

Fermentingbarrels

Fermentationroom Dry storage

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

Micrococcaceae 0.09 0.08 0.01 0.01 0.24 0.33 0.08 0.03 0.07 0.07 0.16 0.08Moraxellaceae 0.31 0.27 0.35 0.20 0.04 0.04 0.02 0.02 0.08 0.06 0.08 0.12Bacillaceae 0.00 0.00 0.01 0.01 0.00 0.01 0.08 0.09 0.02 0.00 0.09 0.18Lactobacillales 0.02 0.04 0.01 0.01 0.20 0.11 0.08 0.04 0.07 0.09 0.08 0.06Pseudomonadaceae 0.09 0.13 0.21 0.10 0.02 0.03 0.05 0.00 0.02 0.01 0.03 0.02Lactobacillaceae 0.01 0.02 0.01 0.01 0.11 0.10 0.08 0.09 0.05 0.04 0.11 0.11Methylobacteriaceae 0.08 0.12 0.01 0.01 0.01 0.01 0.01 0.02 0.01 0.00 0.01 0.01Leuconostocaceae 0.01 0.01 0.00 0.00 0.12 0.08 0.06 0.05 0.06 0.07 0.09 0.06Streptococcaceae 0.00 0.00 0.00 0.00 0.01 0.01 0.10 0.12 0.00 0.00 0.00 0.00Alteromonadaceae 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Enterobacteriaceae 0.07 0.07 0.08 0.04 0.02 0.02 0.03 0.00 0.02 0.02 0.01 0.01Rhodospirillaceae 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.10 0.00 0.00Sphingomonadaceae 0.07 0.05 0.02 0.01 0.03 0.02 0.02 0.03 0.11 0.02 0.03 0.03Propionibacteriaceae 0.00 0.00 0.00 0.00 0.01 0.01 0.12 0.02 0.01 0.01 0.01 0.01Rhodobacteraceae 0.01 0.01 0.00 0.00 0.03 0.05 0.01 0.00 0.03 0.00 0.02 0.01Rhizobiaceae 0.04 0.04 0.04 0.02 0.03 0.05 0.01 0.01 0.01 0.00 0.01 0.01Xanthomonadaceae 0.03 0.05 0.01 0.01 0.01 0.02 0.01 0.00 0.02 0.02 0.01 0.01Comamonadaceae 0.03 0.03 0.05 0.02 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01Veillonellaceae 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.05 0.00 0.00 0.00 0.00Nakamurellaceae 0.02 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Dermabacteraceae 0.00 0.00 0.00 0.00 0.01 0.02 0.01 0.00 0.00 0.00 0.01 0.01Microbacteriaceae 0.02 0.02 0.01 0.01 0.01 0.01 0.02 0.02 0.03 0.02 0.02 0.01Chthoniobacteraceae 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Staphylococcaceae 0.00 0.00 0.00 0.00 0.01 0.01 0.04 0.01 0.01 0.00 0.01 0.01Halomonadaceae 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Corynebacteriaceae 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.02 0.01 0.00 0.00 0.00Planococcaceae 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.02 0.02Oxalobacteraceae 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.02 0.01 0.01 0.01Other 0.12 0.09 0.18 0.13 0.09 0.06 0.11 0.01 0.26 0.09 0.18 0.09

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which colonized the main warehouse, dry storage areas, the lobby, and several doorhandles throughout the facility. Interestingly, this genus is in low abundance in thepacking and processing rooms, raw vegetables, and the fermentation room directlyabove the walk-in refrigerator. The fermentation room is part of a new addition to thefactory and had not yet been used to house fermentation barrels at the time ofsampling. This is significant, as Leuconostoc spp. permeate the environment as a resultof fermentation to potentially influence subsequent processes. Accordingly, Leucono-stoc and Lactobacillales spp. dominated the areas where natural fermentation occurred.It is noteworthy that the sole exception is the dry storage area below the walk-inrefrigerator where a relatively high abundance of Bacillus spp. was identified. This areaexperiences a degree of foot traffic, and thus, soil harboring Bacillus spp. was likelydeposited here. A similar observation was made on the floors between the barrels inthe fermentation room with lower Bacillus representation relative to other floor areasin the same room. Differences in the surface topologies are also known to influenceBacillus colonization and thus may aid survival under both arid and humid conditions(21). Chryseobacterium spp., Pseudomonas spp., and Enterobacteriaceae were detectedin a nonrandom colonization pattern. These taxa likely flowed in from the raw cabbageentering the facility, as they were detected on the raw vegetables and processingequipment with which they were in contact. Accordingly, fresh fruit and vegetablesoften carry high levels of Enterobacteriaceae and Pseudomonas spp. as commensalmicrobiota; Chryseobacterium and Pseudomonas spp. have also been recovered fromsalads (22–24). These taxa, however, were not observed in high abundance on the floor

FIG 6 Spatial distribution heatmap of bacteria in the fermentation facility environment. (a) Plots indicate relative abundances of genera as a percentage of theentire community. The scale bar on top of each map normalizes the relative abundances of the defined taxa. In the Leuconostoc map, for example, the darkestgreen color indicates that the walk-in refrigerator community was 19% Leuconostoc. In Staphylococcus, the darkest green color represents 4% of the totalcommunity structure. (b and c) Predicted microbial contamination sources within the vegetable fermentation facility. The maps illustrate percentages ofpredicted sources for members of the community, as estimated by SourceTracker. The scales above the maps represent the percentage of the total communitycomposition for which the source accounts; data are shown for raw vegetables (blue) (b) and unknown origins (orange) (c).

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or doors, suggesting that they are transferred via direct contact with vegetables andnot by human handling. This is supported by k-mer species-specific identification,where the cabbage swab samples yielded Pseudomonas protegens (2.99% of bacterialreads) and a low signal for Pseudomonas putida (0.54%). Further evidence includes P.protegens (0.93%) and Pseudomonas fluorescens (0.45%) in the sink sample and a largecontribution by P. fluorescens (10.68%), with lesser concentrations of P. putida (4.04%),P. protegens (1.38%), and Pseudomonas synxantha (1.22%) on the swabbed shreddersurface (Fig. S13). In addition, and counter to expectations, the raw vegetables exhib-ited a low relative abundance of LAB (25). Thus, the LAB that colonize the facility werepotentially acquired from increased populations as a result of fermentation (17, 25–27).An alternative explanation is that the LAB adhere tightly to the vegetable substratumand thus were not sampled by swabbing. That both occur simultaneously is supportedby time-course k-mer analysis, where large amounts of LAB k-mer reads were found inthe heavily mixed sauerkraut product at time zero in fermentation. Among the speciessequences scored, 55.74% of sequence reads matched Lactococcus lactis subsp. lactis,11.65% matched Lactobacillus plantarum, and 18.22% matched Leuconostoc mesen-teroides (Fig. S13). Another interesting observation is that small concentrations ofStaphylococcus spp. (�0.01%) were identified in fermentation barrels and bathroomfloor swab surfaces (Fig. 4). These barrels were recently acquired and thus may haveStaphylococcus contamination from handling during their manufacture.

The spatial distribution of bacteria within this enclosed facility is intrinsically relatedto the particular surface type colonized. This provides information about the microbialcommunity structure of the built environment that may influence the food fermenta-tion process. It is clear that fermentations impact the colonization pattern within thefacility. These results may provide insight into the microbial communities that colonizesurfaces that are cleaned more regularly than are the walls and ceilings. The foodpacking and processing room is maintained at good manufacturing practice (GMP)standards for cleanliness, which includes routine floor cleaning, and employees mustwash their hands, wear gloves, and wear hair protection before entering. It is possiblethat the dry storage and fermentation areas accumulate raw vegetable material tomaintain environmental lactic acid bacterial populations. This phenomenon is sup-ported by previous studies, which suggest that bacterial communities are establishedon food production facility surfaces despite routine cleaning (11). This also suggeststhat cleaning practices are essential for maintenance of this low-diversity environmentto prevent the colonization of other species that could disrupt the microbial commu-nity structure in this environment (11). Clearly, natural fermentation promotes aparticular microbial consortium within the facility, generally in the absence of typicalhuman skin bacteria, such as members of the families Streptococcaceae and Propi-onibacteriaceae. Some of the literature suggests that human skin is a primary source ofbacterial contamination and can be transferred to environmental surfaces (14), such asfacility areas that receive the most human contact. Environmental microbiology studiesexperience a potential bias in operational taxonomic unit (OTU) representation due tocell lysis, sample collection, storage, PCR, and other aspects of sample preparation (28).In this study, we have followed the protocol adapted by Bokulich and Mills that hasbeen employed in subsequent studies (11, 13).

Source tracking reveals origins of a diverse set of colonizing microbiota. ABayesian method (i.e., SourceTracker; see Materials and Methods) was used to furtherinvestigate the origins of microbial diversity within the facility (29). The SourceTrackerapproach is used in studies of the built environment. Its utility is in identifying thepotential origin of bacterial contamination. This tool predicts the percentage of areservoir community originating from a given source, which is defined a priori. Modelsource samples were derived from the raw cabbage and vegetable handling surfacedata obtained in this study. For human skin profiles, we compared our study toreference human skin profiles that were previously characterized (30). Figure 6b depictsthe distribution of raw cabbage-colonizing microbiota on several surfaces near the

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packing and processing area. Locations farther from the processing area had a greaterabundance of bacteria with unknown origins (Fig. 6c). These bacteria likely originatefrom the fermentation process itself and were not used to train the Bayesian modelingtool. Again, the processing room and vegetable surfaces differ in microbial composi-tion, with human-associated microbiota contributing a very minor source of contami-nation. This is significant in that hygienic practices appear to be effective with thefermentation process restricting those human-associated microbes that may be able tocolonize environmental surfaces.

Microbial succession within a newly established fermentation room. The newlyestablished fermentation room (described above) was resampled (n � 15) �5 monthssubsequent to an initial characterization. This room is part of a new addition to thefacility and was not previously used for fermentations. Swabs were obtained frombarrels, as well as the walls, floors, and ceiling in order to provide greater resolutionwithin the relatively small environment. These 15 samples were compared with 9samples collected previously from the same room in order to track changes over time.

Weighted UniFrac distances, visualized as a principal-coordinate analysis (PCoA)plot, indicate the segregation of raw vegetable microbiomes from other communityassemblages (Fig. 7a). More than 67% of the variability is captured by the first twoprincipal components (PC1 and PC2). We also identified a single outlier community thatwas sampled from a chopped cabbage mix rather than a swab of the surface. Thissuggests that bacteria that strongly adhere or colonize internal structures within thevegetable are more likely to impact the built environment. Similarly, comparisons ofthese communities of sequence-based clusters, or operational taxonomic units (OTUs),reveal distinct differences between the two sampling points (Fig. 7b). PC1, whichexplains 30.19% of the variation, segregated the two sampling points when usingweighted UniFrac distances. OTUs were identified taxonomically and were examined atthe family level (Fig. 8). The pretransition floor, ceiling, and walls exhibited greatercommunity diversity than did posttransition surfaces, as reflected by a high Shannondiversity index and species richness on these surfaces (Fig. S6). These microbial com-munities were composed of the bacterial families Enterobacteriaceae, Pseudomon-adaceae, Streptococcaceae, Micrococcaceae, and Lactobacillaceae, and several otherlower-abundance taxa (Fig. 8). In contrast, communities observed on unused fermen-tation barrels were more homogeneous and hosted significantly lower diversity andevenness than communities in postfermentation barrels (Fig. S6). The pretransition

FIG 7 (a) Principal-coordinate analysis of environmental swab surfaces. Weighed UniFrac distances were used to assess beta diversity. Rawvegetable swabs (blue circles) are genetically distinct from environmental swabs (red circles). Approximately 67% of the variability can beexplained by the first two principal components (PC1 and PC2). (b) Principal-coordinate analysis among pre- and postfermentationsurfaces. A high degree of genetic dissimilarity between the prefermentation (blue circle) and postfermentation (red circles) environmentalsurfaces was observed. Approximately 50% of the variability can be explained by PC1 and PC2.

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swabs from the barrels were composed mainly of Pseudomonadaceae (20% comparedto 7%), Micrococcaceae (22% compared to 1%), Lactobacillaceae (13% compared to0.1%), Lactobacillales (27% compared to 0.1%), and Leuconostocaceae (8% compared to0.2%) compared to posttransition barrels (Table 2). These bacterial families in prefer-mentation barrels were most like the built environment surfaces observed in the rest ofthe food production facility. Postfermentation room barrels were significantly domi-

FIG 8 Family-level comparisons between prefermentation and postfermentation room environmental swab surfaces. Eachstacked bar represents mean abundance of 3 surface swabs from same area. Vertical red line segregates the surfaces.

TABLE 2 Relative abundances of the bacterial families in pre- and postfermentation room surfaces at family level

Surface

Abundance by surface

Barrel pre Barrel post Ceiling pre Ceiling post Floor pre Floor post Wall pre Wall post

Floorbetweenbarrels post

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

Comamonadaceae 0.0 0.0 0.2 0.1 0.0 0.0 0.4 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0Enterobacteriaceae 0.0 0.0 0.1 0.1 0.1 0.0 0.0 0.0 0.1 0.0 0.1 0.1 0.1 0.0 0.1 0.0 0.6 0.3Oxalobacteraceae 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.1 0.0Bacillaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.1 0.0 0.0 0.0 0.0 0.0 0.0Pseudomonadaceae 0.2 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0Lactobacillaceae 0.1 0.1 0.0 0.0 0.3 0.2 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0Micrococcaceae 0.2 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0Moraxellaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.2 0.2Lactobacillales 0.3 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Leuconostocaceae 0.1 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0Propionibacteriaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Streptococcaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0Streptophyta 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0Sphingomonadaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Microbacteriaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Planococcaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Xanthomonadaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Nocardioidaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Staphylococcaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Sphingobacteriaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Corynebacteriaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Nocardiaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Rhodobacteraceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Paenibacillaceae 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

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nated by Oxalobacteraceae (23%), Comamonadaceae (16%), and Enterobacteriaceae(11%) compared to �0.1% abundance in prefermentation room barrels (Fig. 9 andTable 2). Following transition to an active fermentation room for about 5 months, themicrobial community compositions shifted appreciably. Diversity indices significantlydecreased for posttransition surfaces compared to pretransition surfaces, except forbarrels (Fig. S6). Walls, floors, and spaces between the barrels following transition to anactive fermentation room were segregated into distinct clusters relative to pretransitionsurfaces (Fig. S7). At this point, floor surfaces were significantly enriched (P � 0.05) withOxalobacteraceae (37% compared to 3%), and Bacillaceae (40% compared to 2%)relative to prefermentation floor surfaces, whereas Moraxellaceae (6 to 7%) and Enter-obacteriaceae (6 to 8%) were detected in similar concentrations (Fig. 9 and Table 2). Thefloor between the barrels posttransition was segregated distinctly due to high abun-dances of Enterobacteriaceae (58%), Moraxellaceae (21%), and Leuconostocaceae (8%) com-pared to both pre- and posttransition floor surfaces (Fig. 9 and Table 2). Of note, the floorbetween barrels was not sampled prior to the transition to a fermentation room. In contrast,these posttransition surfaces were distinct from pre- and posttransition floor surfaces. Thus,floor surfaces were distinguishable within the same room based on the sampling location.The low abundances of Oxalobacteraceae (3%) and of Bacillaceae (2%) on the floor betweenthe barrels relative to other floor areas in the room may be due to differences in accessto the floors by foot. These bacterial families are generally associated with soil surfaces. Theswabs collected from the posttransition wall surfaces were significantly enriched (P � 0.05)with Comamonadaceae (53% compared to 3%), and in pretransition wall surfaces, highabundances of Lactobacillaceae (11% compared to �1%) and Micrococcaceae (12% com-pared to 4%), were detected compared to posttransition wall surfaces, suggesting the

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FIG 9 Mean relative abundances of the bacterial families in pre- (pre) and postfermentation (post) room surfaceswabs. One-way ANOVA with a post hoc Tukey test was performed, with significance at a P value of �0.05. a top, significant difference of mean relative abundances for bacterial families between two surface swabs. Forexample, a, b on the top of bars represents that family Comamonadaceae is significantly different between wallpostfermentation and prefermentation surfaces as well as ceiling postfermentation and prefermentation surfaces.a, wall post versus wall pre; b, ceiling post versus ceiling pre; c, barrel pre versus floor between barrels post; d, floorbetween barrels post versus floor post; e, floor post versus floor pre; f, barrel post versus barrel pre; g, floorbetween barrels post versus floor post; h, floor post versus floor pre; i, barrel pre versus floor between barrels post;j, barrel post versus barrel pre; k, ceiling post versus ceiling pre; l, barrel post versus barrel pre; m, barrel pre versusfloor between barrels post; n, barrel post versus floor between barrels post; o, barrel pre versus floor betweenbarrels post; p, barrel post versus barrel pre.

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bidirectional flow of environmental microbial populations to the newly established fermen-tation room (Fig. 9 and Table 2). It was somewhat surprising that LAB were found in higherabundances prior to active fermentations. Although a decrease in Lactobacillaceae abun-dance is likely explained by the accompanying increase in bacterial diversity following thearea’s transition, a more diverse community may restrict the Lactobacillus populations inthis stable ecosystem.

Interestingly, the walls and ceiling surfaces sampled posttransition contained between20% and 70% of an unidentified genus within the family Comamonadaceae (Fig. 8). Thesebacteria have been detected in low abundance in the raw vegetables and vegetablehandling surfaces (3 to 5%) compared to dry storage and processing room surfaces (Fig. 5and Table 1). This suggests that these bacteria originated from raw cabbage and vegetablehandling surfaces rather than another exogenous source. Using nonmultidimensionalscaling (NMDS), the comparison between the fermentation room pre- and postactivesurfaces also revealed that these groups segregate from each other (Fig. S8a). Analysis ofsimilarity (ANOSIM) using Bray-Curtis distances determined that the sampling points aresignificantly different (R � 0.2, P � 0.05) from each other compared to within-groupdifferences (Fig. S8b). These groups were significantly different in Shannon diversity indexand species richness (P � 0.05), with similar levels of species evenness in the two groups(Fig. S9). This suggests that diversity and species richness were not significantly differentdue to uneven species distribution but rather due to distinct microbial community com-positions among pre- and postfermentation surfaces.

Linear discriminant analysis effect size (LEfSe) analysis at the family level identifiedLactobacillaceae, Leuconostocaceae, and Micrococcaceae as highly abundant in prefer-mentation surfaces compared to Paenibacillaceae in postfermentation surfaces (Fig.S10). A log odds ratio plot indicated that these could be defined as biomarker familiesto be used to segregate groups and that can potentially identify risk to product andmicrobial ecology of that surface (Fig. S11). As depicted in Fig. S10 and S11, Lactoba-cillaceae, Leuconostocaceae, and Micrococcaceae families are biomarkers for prefermen-tation room surfaces compared to Oxalobacteraceae, Comamonadaceae, Bacillaceae,and other families in postfermentation surfaces.

Accordingly, these fermentation-associated families may be monitored within thefacility to determine if there is a high abundance of any undesirable bacterial popula-tions. A detailed description of all bacterial families identified is provided in Fig. S11.

Metagenomic analysis of the bacterial succession within the fermentationproduct. A metagenomic sequence analysis of the postfermentation food product wasperformed to determine changes of dominant and minority bacterial species duringfermentation. This approach also enabled a comparison with the 16S rRNA phyloge-netic profile of the facility microbiome. Samples were collected at 0, 5, 8, and 11 weeksfrom the sauerkraut fermentation initiated on 27 October 2016. The k-mer analysisrevealed that species from the genera Lactobacillus, Lactococcus, and Leuconostoc weremost abundant in each sample. A substantial decrease in the abundance of Lactococcuslactis subsp. lactis occurred over time. This species was most abundant at time zero (T0)(55% abundance) and decreased to 30% by 11 weeks. Similarly, Lactobacillus plantarumabundance increased from 12% to 31% by 8 weeks and decreased to 24% by 11 weeks.Leuconostoc mesenteroides decreased from 18% at T0 to 11% by 11 weeks. Lactobacillusrhamnosus was surprisingly detected at 11% abundance at 11 weeks, whereas it wasdetected below 0.1% in all other samples (T0 to 8 weeks). This indicates that L.rhamnosus was not typically found in the production facility and was detected only inthe final product. Lactobacillus brevis experienced an �4-fold change in abundance (6%to 23%) from T0 to 5 weeks and decreased slightly (18 to 16%) from 8 to 11 weeks. Ofnote, other species of Leuconostoc and Lactobacillus did not substantially change overthis time course and were low (1 to 3% matched reads) or at the limits of detection (0.12to 0.16%) at all time points (Fig. S12). We saw surprisingly strong LAB sequence predom-inance in the T0 samples. From other fermentations, we see anaerobic plate counts(predominantly LAB) of 105 after mixing all fermentation components. The level of LAB isinitially low and increases for some species of LAB and increases in different phases for

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different LAB. The k-mer-based identification performed on the raw cabbage mix, shredder,and a recently used clean sink revealed that all samples exhibited distinct microbial profilesfrom each other (Fig. S13). It is noted that fermentation samples yielded millions ofsequence reads, with more than 40% of the reads matching bacterial k-mer database. Thesequence determinations from the swab sampling produced a high failure rate for DNAyield (about 50% for 32 samples) with a subsequent 50% failure rate for library andsequence production. The DNA yield for the samples that worked ranged between 1.26 to6.64 ng/�l. The DNA yield from swabs when the library preparation was not successful wasless than 1 ng/�l. An additional consideration for the whole-genome data produced fromlow-DNA swab samples was that the swab sample obtained sequences that contained lessthan a 2% match to the k-mer database compared to the fermentation samples (40%bacterial database identification [ID]).

Summary of study conclusions. Spontaneous fermentation transforms raw vege-tables into pickled foods primarily with the aid of lactic acid bacteria. Lactic acidbacteria are acid-tolerant anaerobes, which flourish in this environment and quicklybloom to dominate the pickling vegetable microbiome. However, it was unclear if thesemicrobial communities originate from the raw vegetables or if these bacteria arelaterally transferred between the food products within the built environment. Withinthis facility, microbial communities are enriched with the phylum Proteobacteria andhave a low abundance of Firmicutes on raw vegetables and the vegetable handlingsurfaces. Moreover, these communities are significantly different when contrasted withthe fermentation room, processing room, and dry storage surfaces. Multivariate analysisidentified that raw vegetable and vegetable handling surfaces were more similar toeach other than the fermentation room, processing room, and dry storage surfaces. Asurface-specific microbial profile was characterized among various locations within thefacility. Source tracker analyses determined that raw vegetables were the main sourceof microbes rather than human handling and that handling likely constitutes a veryminor contaminant within the facility. Raw vegetable surfaces exhibit low diversityindex and species richness compared to the high microbial diversity in all otherenvironmental surfaces. Biomarker analysis identified that the phylum Proteobacteria islikely to be associated with raw vegetable surfaces relative to environmental surfacesthat are linked with Firmicutes. Microbial diversity analysis performed on a fermentationprior to and after transition to usage revealed that diversity and richness were high inpretransition compared to posttransition surfaces. After the new fermentation roomtransitioned to active production, its resident microbiome was less diverse. The estab-lishment of an active fermentation room reduced the overall microbial diversity, whichis similar to what was observed in the raw vegetable surfaces rather than otherenvironmental surfaces. It is possible that new resources introduced to the roominfluenced the microbial composition. Moreover, the room’s microbiome structure mayhave been in a transient state during previous sampling, with the populations shiftingtoward other more diverse areas of the facility posttransition. This hypothesis could befurther tested in the future through judicious sampling of areas undergoing similartransitions. Moreover, this particular microbial environment could be compared withthose involving other vegetable fermentations. In general, the lactic acid bacteriacomprise a large portion of the core community established in the production facility.Lactic acid bacteria were not observed in high abundance on raw vegetables, which isconsistent with the expected enrichment as a consequence of fermentation. Thus, thehigh abundance of LAB in fermentation vessels is likely responsible for distributingthese fermentative microbes to the rest of the facility environment.

MATERIALS AND METHODSFacility description. The production facility specializes in spontaneously fermented vegetables and

consists of a storage area for processing materials, a processing room where food is prepared, and severalfermentation rooms that are maintained at 18 to 27°C, allowing for vegetables to ferment undisturbedin barrels. The facility areas that receive the most human contact are the processing room and thestorage room, where there is a large door through which vegetables are delivered to the factory.

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Sauerkraut production. Raw cabbages are chopped, shredded with an industrial food shredder, andlayered in barrels with salt and freshwater. Full barrels of shredded cabbage are covered with water bagsfor the duration of the fermentation, creating an airtight seal. After approximately 6 weeks, when a batchpasses a taste test and pH test, the finished sauerkraut barrels are brought back to the processing roomand hand-packaged into jars for distribution. (Of note, sauerkraut typically ferments for a minimum of 6weeks but often ferments for several months.) Other fermented vegetable products are prepared in asimilar way, with some added ingredients and differing fermentation times.

Sample collection areas. Samples were collected from storage area floors, the processing room,vegetable processing equipment, fermentation barrels, door handles, and raw vegetables subsequent todelivery.

Sample collection and DNA extraction. In total, 56 samples were collected for further analysis (Table3). This includes 32 samples collected from different surfaces within the facility (Fig. 1), with another 9 samplesfrom a newly established fermentation room during a single visit (n � 41). A total of 15 new samples werecollected �5 months later (n � 15). These 15 samples were collected from the newly established fermentationroom in order to track changes over time. This room was part of a new addition to the facility and was notpreviously used for fermentations. These 15 swabs were obtained from barrels, walls, floors, and the ceilingto provide greater resolution within the facility among pre- and postproduction environments. The facility wasin full operation at the time of sampling. Surfaces were swabbed using sterile cotton-tipped wooden swabs(Puritan Medical, Guilford, ME), which were moistened with sterile 1� phosphate-buffered saline (PBS)solution (Fisher BioReagents, Fair Lawn, NJ). Swabs were firmly pressed against surfaces and streaked inoverlapping S strokes while rotating the swab to ensure full contact. Approximately 25.8 cm2 of area wassampled per swab. Wooden swab tips with the cotton applicator were snapped off into sterile 1.5-mlpolyethylene tubes containing 0.75 ml of sterile PBS, using the plastic lid to avoid manual contact. Swabs andtubes were stored at �20°C prior to analysis (11). Total DNA was extracted from swab tips with a PowerLyzerPowerSoil DNA isolation kit (Mo Bio, Carlsbad, CA) by placing the entire swab tip along with PBS in theextraction tube and following the manufacturer’s protocol, with the addition of a bead-beating stepconducted on a FastPrep-24 instrument (MP Biomedicals, Santa Ana, CA) at 4.5 m/s for 45 s. One hundredmicroliters of purified DNA was eluted in pure H2O. The eluate was further concentrated to approximately 25�l with a Vacufuge plus (Eppendorf, Hamburg, Germany) for 30 min at 45°C on the V-AQ setting. Purified DNAwas stored at �20°C for further processing.

16S rRNA amplicon sequencing library construction. Purified nucleic acids were used as atemplate (0.1 to 11.0 ng total DNA) to PCR amplify the V3-V4 region of the 16S rRNA, as well as theIllumina overhang adapter in preparation for sequencing. The primer set developed by Illuminawas FwOvAd_341F (5=-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG) andReOvAd_785R (5=-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC).

PCR amplification was performed using 2� HiFi HotStart ReadyMix (Kapa Biosystems, Wilmington, MA,USA) under following conditions: 95 C for 3 min, 25 cycles of 95°C for 3 s, 55°C for 30 s, and 72°C for 30 s, and72°C for 5 min. AMPure XP beads (Beckman Coulter, Danvers, MA, USA) were used to purify the PCR productsfrom free primers and other contaminants. A second PCR was performed to attach dual indices and Illuminasequencing adapters using the Nextera XT index kit (Illumina, San Diego, CA, USA), followed by a secondround of AMPure XP bead purification. PCR products were quantified using the Qubit double-stranded DNA(dsDNA) BR assay (Life Technologies, Carlsbad, CA, USA). The quality of PCR products was measured by DNAanalysis ScreenTape assay on the TapeStation 2200 system (Agilent Technologies, Santa Clara, CA, USA). PCRproducts were pooled in equimolar concentration (4 nM) and denatured immediately prior to sequencing.Sequencing was performed on an Illumina MiSeq platform (paired-end, MiSeq reagent kit v3, 10% Phi-X) atthe Genomics Resource Laboratory, University of Massachusetts Amherst, and at the FDA WEAC laboratories.In total, 14,355,685 paired-end sequences were generated.

16S rRNA gene bioinformatic and statistical analyses. Illumina paired-end reads were qualityfiltered and analyzed using Qualitative Insights into Microbial Ecology (QIIME) v1.9.0 (31). Raw forwardand reverse reads were aligned using fastq-join (32) and combined into a single fastq file using thesplit_libraries tool, which truncates reads with three consecutive base calls that exhibit a Phred scorebelow 19. In total, 8,839,451 sequences (61.57% of total) were assembled and deemed passable followingquality filtering. Reads were then subjected to open reference operational taxonomic unit (OTU) pickingusing the QIIME pipeline, implementing the uclust alignment algorithm at 97% identity, against theGreenGenes database, 13_8 release. It is noted that step 4 of the pick_open_reference_otus.py pipelinewas omitted due to the large size of the data set. OTUs that were identified as chloroplasts ormitochondria and with fewer than 10 assigned sequences were removed from the OTU table to minimizeinflated diversity estimates (33). A phylogenetic tree of final OTU alignments was generated withFastTree. The quality-filtered OTU table contained 4,939,277 sequences (55.87% of input) distributedacross 5,388 OTU, with a table density of 0.2. The table was rarified to 5,524 sequences for subsequentanalyses (Fig. S1). Relative abundance values were calculated by reads per OTU by the total reads in thatsample. Data visualization was performed using Emperor (https://biocore.github.io/emperor/) and RStudio. Statistical analysis for multiple test corrections was carried out using GraphPad Prism (version7.0). Alpha- and beta-diversity analyses were carried out using weighted UniFrac distance betweensamples for bacterial 16S rRNA sequences. Principal-coordinate analysis (PCoA) was computed using aUniFrac distance matrix. Hierarchical clustering analysis was performed using the relative abundance ofOTUs, and a heat map was generated in the R library package. Analysis of similarity (ANOSIM) with 999permutations was used to test significant differences between surface groups based on weightedUniFrac distance matrices. Similarity percentage analysis (SIMPER) on Bray-Curtis distances was used toidentify the main contributors of the bacterial families responsible for the differences in the surface types.

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QIIME OTU table and metadata files were further analyzed with the Calypso web server (http://cgenome.net/wiki/index.php/Calypso) (34) for feature selection and multivariate data analysis on group-basedcomparisons. A detailed description of the samples acquired in this study is provided in Table 3.

Fermentation sample preparation and whole-genome sequencing. Thirty-five-milliliter sampleswere clarified twice by 2-min centrifugations at 30 � g (400 rpm), and cells were harvested bycentrifugation for 20 min at 3,000 � g (4,000 rpm). Cell pellets were obtained from 30-ml clarifiedfermentation supernatants and lysed using the DNeasy PowerMax soil kit (8- or 16-g bead extraction),following the manufacturer’s directions (Qiagen, GmbH). DNA was further purified using DNA-Clean &

TABLE 3 Sample description for samples analyzed in this study

No. Name Description Group Collection dateNo. of quality-filtered reads

1 E14 Upper-right fermentation room Environmental 8/9/16 48,2692 E15 Walk-in floor Environmental 8/9/16 297,8943 E17 Space between warehouse and kitchen Environmental 8/9/16 514,5634 E18 Bathroom 1 Environmental 8/9/16 299,9155 E23 Warehouse away from receiving door Environmental 8/9/16 133,2126 E24 Warehouse near receiving door Environmental 8/9/16 23,4807 E25 Barrel forest aisle Environmental 8/9/16 172,2178 E26 Rear of warehouse floor Environmental 8/9/16 174,3889 E30 Lobby floor Environmental 8/9/16 6,71010 E32 Dry storage Environmental 8/9/16 118,45411 E33 Dry storage fermentation barrels Environmental 8/9/16 75,86712 E34 Packing room floor Environmental 8/9/16 270,83713 E36 Floor drain Environmental 8/9/16 170,23414 E38 Kitchen plastic flaps Environmental 8/9/16 33,40615 E39 Mop sink Environmental 8/9/16 495,73416 E40 Store room plastic flaps Environmental 8/9/16 935,93417 E41 Kitchen door push bar Environmental 8/9/16 108,39518 E42 Kitchen door push bar in dry storage Environmental 8/9/16 14,50219 E44 Floor in front of store room entrance Environmental 8/9/16 319,85220 E46 Floor in front of fridge door Environmental 8/9/16 53,99021 E51 Fermentation room barrel Environmental 8/9/16 85,73122 E52 Fermentation room floor Environmental 8/9/16 132,14123 E6 Hand sink Environmental 8/9/16 75,46324 K21 Fresh cabbage peeled Raw vegetable 8/9/16 39,08125 K22 Fresh cabbage outside Raw vegetable 8/9/16 16,15626 K25 Raw cabbage mix, not swab Raw vegetable 8/9/16 142,57127 K28 Mixing bin 1 Raw vegetable 8/9/16 6,62628 K29 Mixing bin 2 Raw vegetable 8/9/16 5,52429 K30 Knife Raw vegetable 8/9/16 17,76230 K32 Shredder Raw vegetable 8/9/16 93,80131 K46 Raw kraut swab Raw vegetable 8/9/16 15,33932 K50 Cutting board Raw vegetable 8/9/16 40,23333 E55 Fermentation room floor Prefermentation room 8/9/16 130,25934 E56 Fermentation room wall Prefermentation room 8/9/16 132,25335 E57 Fermentation room ceiling Prefermentation room 8/9/16 52,84136 E58 Fermentation room floor Prefermentation room 8/9/16 79,54937 E59 Fermentation room wall Prefermentation room 8/9/16 50,42938 E60 Fermentation room ceiling Prefermentation room 8/9/16 108,81339 E61 Fermentation room barrel Prefermentation room 8/9/16 100,55540 E62 Fermentation room barrel Prefermentation room 8/9/16 134,89041 E63 Fermentation room barrel Prefermentation room 8/9/16 54,86342 E64 Fermentation room floor Postfermentation room 1/19/17 185,92743 E65 Fermentation room floor Postfermentation room 1/19/17 61,60244 E66 Fermentation room floor Postfermentation room 1/19/17 193,01545 E67 Wall near door Postfermentation room 1/19/17 87,86446 E68 Wall on back of room Postfermentation room 1/19/17 92,28947 E69 Wall to right of door Postfermentation room 1/19/17 120,74848 E70A Floor in between barrels on left Postfermentation room 1/19/17 78,05349 E70B Floor in between barrels on left Postfermentation room 1/19/17 79,22350 E71 Ceiling in front of door Postfermentation room 1/19/17 119,80951 E72 Ceiling in middle of room Postfermentation room 1/19/17 868,15752 E73 Ceiling in back of room Postfermentation room 1/19/17 49,26653 E74 Floor in between barrels on right Postfermentation room 1/19/17 111,45254 E75 Side of barrel on left Postfermentation room 1/19/17 80,85655 E76 Outside of barrel on right Postfermentation room 1/19/17 116,28056 E77 Barrel top on left Postfermentation room 1/19/17 141,592

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Concentrator-100 cartridges following the manufacturer’s directions (Zymo Research, Irvine, CA). Se-quencing libraries were prepared with the Nextera XT DNA library preparation kit (Illumina). Librarieswere sequenced at the FDA Center for Food Safety and Applied Nutrition (CFSAN) laboratories on aMiSeq system using a MiSeq reagent kit version 2 (Illumina; 500 cycles).

Metagenomic k-mer analysis. Illumina DNA sequencing libraries and sequencing runs were de-signed to generate maximum output from unique small swab samples, or fermentation samples withmore DNA, on the order of 1,000,000 to 10,000,000 reads. Only three of the first 20 factory swab samplescontained enough DNA to generate adequate whole-genome sequencing libraries. Sequencing readswere analyzed for microbial composition using a custom C�� program called k-mer analysis developedat the FDA (CFSAN, OARSA laboratories, M. K. Mammel). The k-mer database is designed to match onlyspecies-specific sequences. The purpose of the k-mer analysis is to detect what species are present andtheir relative abundances. A species-specific k-mer data set was built using 786 complete GenBankcomplete genome entries, averaging 50,899-mers for each species. These included 14 Enterobacter, 20Escherichia, 9 Leuconostoc, 8 Listeria, 24 Pseudomonas, 97 Salmonella, 18 Streptococcus, 15 Vibrio, and twoWeissella species entries. For the 171 Lactobacillus species included, there were on average 57,800 k-merseach, for an average coverage of 2,030,000 bp per genome (Tables S1 and S2). It is designed to match30-base k-mer sequences from sequence reads to a comprehensive sequence collection, a database ofbacterial species. The database was constructed of species-specific 30-bp k-mer signature sequences. Foreach species of interest, each nonduplicated k-mer from a reference whole-genome sequence wasplaced into the database. The k-mers not found in at least 2/3 of a set of additional genome sequencesof the same species as well as k-mers found in genomes of other species were removed. The resultingk-mer database used in this work contains 5,372 target entries, each consisting of approximately 40,000(range, 255 to 80,000) k-mers unique to each species in the database. The result of analysis of a set ofsequence reads from a mixed collection of species is a table showing the number of reads found thatmatch each species in the database. To correct for bias due to differing numbers of k-mers used perdatabase entry and genome size, normalization was performed, and the results are tabulated as thepercent contribution to the microbial population of identified species. A number of different softwarepackages are available to determine species content; other programs for profiling bacterial communitiesfrom short metagenomics shotgun sequencing data containing mixed species include KRAKEN andMetaPhlAn. The majority of the unassigned reads were designated “other” bacterial species from severaltests conducted. The unassigned reads (reads not matching the k-mer database) were analyzed byseveral methods. First, when metagenomic data “en masse” were compared against GenBank data sets(i.e., pairwise alignment), we did not get hits to Brassica. Second, the bacterial content was measuredusing a panel of 40 single-copy genes as phylogenetic markers to determine the percentage of those thatwere of bacterial or other origin. For example, the time zero sauerkraut fermentation contained 16,855bacterial hits, 108 Archaea hits, and 81 Eukaryota hits. The bacterial hits represent 98.89% of the matches,with the Eukaryota 0.47%. Although the Eukaryota class is extremely limited, it does contain Brassica rapa.Third, if you take 50 random reads against the nonredundant GenBank data set, 46 reads give top hitsto expected Lactococcus, Lactobacillus, and Leuconostoc species, and four reads give no hit. We alsomapped the reads against the cabbage genome (Brassica oleracea genome whole-genome sequence[WGS] JJMF01 project). Our analysis suggested that at T0, 0.63% of the reads aligned to the Brassicagenome. We have provided additional analyses details in Tables S1 to S3.

Data availability. Amplicon sequence data have been archived in the Qiita database (https://qiita.ucsd.edu/) under the accession number 11119. Metagenomic WGS sequence data have been archivedin the SRA at NCBI (https://www.ncbi.nlm.nih.gov/biosample/) (food fermentation metagenomes,BioSample accession numbers SAMN08049558, SAMN08049559, SAMN08049560, SAMN08049561,SAMN08109790, SAMN08109791, and SAMN08109792).

SUPPLEMENTAL MATERIAL

Supplemental material for this article may be found at https://doi.org/10.1128/AEM.01680-18.

SUPPLEMENTAL FILE 1, PDF file, 2.4 MB.SUPPLEMENTAL FILE 2, XLSX file, 0.1 MB.

ACKNOWLEDGMENTSJ.E.E. acknowledges an American Society for Microbiology Summer Undergraduate

Research Fellowship. Furthermore, J.E.E. acknowledges the UMass Amherst Common-wealth Honors College for providing a Research Assistant Fellowship as well as theUMass Amherst Center for Agriculture, Food, and the Environment Summer ScholarsFellowship. We acknowledge the Center for Produce Safety (grant SCB14056) forproviding partial funding for this work.

We acknowledge the UMass Amherst Genomics Resource Laboratory for assistancewith genome sequencing. We thank Dan Rosenberg and Katie Korby of Real Pickles foraccess to their production facility and for assistance in sample collection. We acknowl-edge the support of and helpful discussions with members of the Sela Lab and CindyKane for technical assistance.

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The funding organizations had no role in the study design, data collection and analysis,decision to publish, or preparation of the manuscript. The views expressed in this article arethose of the authors and do not necessarily reflect the official policy of the Department ofHealth and Human Services, the U.S. Food and Drug Administration (FDA), or the U.S.Government. Reference to any commercial materials, equipment, or process does not inany way constitute approval, endorsement, or recommendation by the FDA.

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