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Coupled DNA-labeling and sequencing approach enables the detection of viable-but-non-culturable Vibrio spp. in irrigation water sources in the Chesapeake Bay watershed

Abstract

Nontraditional irrigation water sources (e.g., recycled water, brackish water) may harbor human pathogens, including Vibrio spp., that could be present in a viable-but-nonculturable (VBNC) state, stymieing current culture-based detection methods. To overcome this challenge, we coupled 5-bromo-2′-deoxyuridine (BrdU) labeling, enrichment techniques, and 16S rRNA sequencing to identify metabolically-active Vibrio spp. in nontraditional irrigation water (recycled water, pond water, non-tidal freshwater, and tidal brackish water). Our coupled BrdU-labeling and sequencing approach revealed the presence of metabolically-active Vibrio spp. at all sampling sites. Whereas, the culture-based method only detected vibrios at three of the four sites. We observed the presence of V. cholerae, V. vulnificus, and V. parahaemolyticus using both methods, while V. aesturianus and V. shilonii were detected only through our labeling/sequencing approach. Multiple other pathogens of concern to human health were also identified through our labeling/sequencing approach including P. shigelloides, B. cereus and E. cloacae. Most importantly, 16S rRNA sequencing of BrdU-labeled samples resulted in Vibrio spp. detection even when our culture-based methods resulted in negative detection. This suggests that our novel approach can effectively detect metabolically-active Vibrio spp. that may have been present in a VBNC state, refining our understanding of the prevalence of vibrios in nontraditional irrigation waters.

Introduction

As global freshwater resources are rapidly being depleted—due to population growth, climate change, over pumping of aquifers and other factors—states and nations are relying more heavily on nontraditional irrigation water sources (e.g., recycled water, brackish water) to ensure agricultural water security and prevent food insecurity [10, 67]. In some semi-arid and arid regions of the world, brackish water is the only remaining irrigation water source available to farmers [29, 50]. The United States Geological Survey (USGS) defines brackish waters as having a dissolved-solids concentration between 1000 and 10,000 mg/L, which is greater than that of freshwater (> 1000 mg/L), but less than that of seawater (35,000 mg/L) [71]. In the semi-arid and arid regions of the United States and other countries, brackish water use has been largely restricted to relatively salt tolerant crops including cotton, sugar beets, barley, wheat, safflower, sorghum, soybeans and tomatoes [29, 32, 57, 58, 64]. The effects of salt stress on plants when irrigated with brackish water are well described in the literature [27, 70], and multiple mitigation strategies are being explored to enable these water sources to be suitable for irrigation purposes [32, 57, 58, 64].

Besides salinity, brackish water sources are known to harbor important human pathogens. Vibrio spp., for instance, are natural inhabitants of brackish estuaries such as the Chesapeake Bay, and include the following frank pathogens: V. cholerae, V. parahaemolyticus and V. vulnificus [14, 15, 81, 83]. Additionally, Vibrio spp. have been recovered from surface waters, such as rivers, creeks, and irrigation canals [37, 52, 65], as well as reclaimed water [25, 52, 53]. Human Vibrio infections can occur among people consuming raw or undercooked shellfish and among those working or recreating in contaminated waters [14, 15, 17, 56]. If Vibrio-contaminated water is also used to irrigate food crops that are eaten raw, this practice could represent an additional exposure pathway for human Vibrio infections [28, 76].

Hence, there is a need to further our understanding of the prevalence of Vibrio spp. in nontraditional irrigation water sources. Nevertheless, previous studies have provided evidence that Vibrio spp. can enter a viable-but-non-culturable (VBNC) state [3, 13, 36, 49, 55], limiting the ability of traditional culture methods to assess the true prevalence of these microorganisms in water bodies. On the other hand, the use of culture-independent, DNA-based techniques such as PCR and sequencing alone do not provide information on the viability of detected vibrios in water sources, since DNA detected through these methods can be derived from either dead or live organisms [40, 43, 84].

This challenge can be addressed by using RNA-based sequencing approaches instead of DNA-based methods, particularly those targeting mRNA (which is only produced by metabolically-active cells), thus indicating the presence of live cells [1]. However, use of environmental RNA has received little attention mainly due to the observation that the persistence of RNA outside the organism is short-lived [60]. This notion has been challenged by Cristescu [16], who provided evidence that RNA may be abundantly and sufficiently present in the environment to evaluate the presence of live organisms [16]. Another major issue, however, is that high quality RNA extraction is more challenging than DNA extraction due to the rapid degradation of RNA which can occur because of inadequate sample processing and/or storage, or contamination with RNA degrading enzymes like RNases [73].

An alternative to RNA-based sequencing methods for the detection of live or metabolically-active bacteria includes the use of DNA labels such as 5-bromo-2’deoxyuridine, (BrdU). BrdU is a synthetic thymidine analog that incorporates into replicating DNA; therefore, bacteria detected in BrdU-treated samples are interpreted to be metabolically-active, viable members of the tested bacterial community [77]. This BrdU labeling technique has been used to identify the metabolically-active fraction of bacteria present in aquatic and soil environments [44, 75, 77]. Thus, the goal of this study was to evaluate whether coupling 5-bromo-2′-deoxyuridine- (BrdU) labeling with next-generation sequencing methods could enable the detection of VBNC vibrios, as well as the differentiation between metabolically-active and dead organisms in nontraditional irrigation water sources.

Materials and methods

Sampling sites and sample collection

Existing sampling sites characterized by CONSERVE: A Center of Excellence at the Nexus of Sustainable Water Reuse, Food and Health (www.conservewaterforfood.org) were leveraged for this study: one tidal brackish water river, one non-tidal freshwater creek, one agricultural pond and one water reclamation facility. Preliminary biweekly bacterial monitoring data from these sites for the period of September 2016 to September 2017 (data not shown) revealed the presence of Vibrio spp. Hence, 4 L grab samples from each site were subsequently collected over the course of 5 months (May 2018 to September 2018) (total n = 30 grab samples) to further characterize the presence of Vibrio spp. via culture-dependent and -independent methods.

Additionally, throughout our sampling period, the following water quality parameters were measured in triplicate using a ProDSS digital sampling system (YSI, Yellow Springs, OH, USA): water temperature (°C), conductivity (SPC uS/cm), pH, dissolved oxygen (%), oxidation/reduction potential (mV), turbidity (FNU), nitrate (mg/L), and chloride (mg/L). Precipitation (inches) data within the last 14 days were also obtained from Weather Underground (https://www.wunderground.com/).

Sample processing

All samples were subjected to both BrdU labeling (1500 mL) and non-labeling (control subsamples, 1500 mL). Our sample processing method is summarized in Supplementary Figure 2.

Non-BrdU labeled water samples

Three 500 mL aliquots of each water sample were filtered through a 0.2 μM filter and then subjected to one of the following: 1) enrichment with alkaline peptone water (APW) (30 mL); 2) enrichment with estuarine peptone water (EPW) (30 mL); or 3) no enrichment (control sample). These samples were incubated for 24 h in the dark at room temperature.

BrdU labeled water samples

Three separate 500 mL aliquots of each water sample were subjected to BrdU treatment using our previously published method [44]. Briefly, 100 μL of 100 mM BrdU was added per 500 mL of water and incubated for 24 h in the dark at room temperature along with the non-BrdU labeled samples. No additional nutrients or carbon sources were added to the samples in order to maintain the original water conditions as much as possible. After incubation, each 500 mL BrdU-labeled water sample was filtered through a 0.2 μM filter and subjected to one of the following: 1) enrichment in APW; 2) enrichment in EPW; or 3) no enrichment (control sample).

Sample incubation and cultivation

All enrichments (BrdU labeled or not) and non-enriched control samples were incubated at 30 °C for 18–20 h. A loopful of growth from the enriched water samples (non-BrdU treated) was then streaked onto thiosulfate-citrate-bile salts-sucrose (TCBS) agar and incubated for 16–24 h at 35 °C. All colonies presenting as yellow (sucrose positive) or green (sucrose negative) on TCBS were selected and subjected to three rounds of streaking for purification and isolation. DNA of resulting purified isolates was then extracted using a heat shock method, which involves isolates being exposed to 100 °C heat and then transferred to ice.

Multiplex PCR detection of Vibrio genus

To detect five pathogenic Vibrio species, a multiplex PCR amplification of the heat shocked isolates was performed following a published protocol [38]. The amplified products were then viewed via gel electrophoresis.

DNA extraction

DNA extractions on all enriched and non-enriched BrdU-labeled and non-labeled water samples were performed using protocols previously published by our group [11, 12]. Briefly, 1 mL of PBS was added to lysing matrix B tubes (MP Biomedicals, Solon, OH, USA) containing: 1) filters (non-enriched samples); or 2) cells that were pelleted by centrifuging at 2450 x g for 20 min (enriched samples). Then, enzymatic cocktails containing lysozyme, mutanolysin, proteinase K and lysostaphin were added to the tubes and the tubes were incubated, after which the cells were mechanically lysed at 6.0 m/s for 40 s using an MP Biomedical FastPrep 24 (Santa Ana, CA, USA). The DNA was then purified using the Qiagen QIAmp DNA mini kit (Germantown, MA, USA) per the manufacturer’s protocol.

Immunocapture of BrdU-treated samples

Immunocapture and isolation of BrdU-labeled were performed using our previously published methods [44]. Briefly, sheared and denatured herring sperm DNA (HS DNA) and monoclonal anti-BrdU (α-BrdU) antibody was mixed in a 1:9 ratio and incubated for 1 h at room temperature. Then DNA extracted from BrdU samples was denatured by heating for 5 mins at 100 °C and transferred onto ice for 5 mins. To this denatured sample DNA, HS DNA/ (α-BrdU) antibody complex was added and incubated for 1 h in the dark at room temperature with agitation to form DNA/HS DNA/ (α-BrdU) antibody complexes. Meanwhile, magnetic beads (Dynabeads, Dynal Inc., Invitrogen by Thermofisher Scientific) coated with goat anti-mouse immunoglobulin G were washed three times with 1 mg/ml acetylated bovine serum albumin (BSA) in phosphate-buffered saline (PBS) using a magnetic particle concentrator. The washed Dynabeads were then added to the DNA/HS DNA/ α-BrdU antibody complexes and in- cubated for an additional 1 h in the dark at room temperature. After incubation, the samples were washed in 0.5 ml PBS-BSA, and the BrdU-containing DNA fraction was eluted by adding 1.7 mM BrdU (in PBS-BSA) and incubating for 1 h in the dark at room temperature.

16S rRNA gene amplification and sequencing

Extracted DNA was then PCR amplified for the V3-V4 hypervariable region of the 16S rRNA gene using the universal primers 319F (ACTCCTACGGGAGGCAGCAG) and 806R (GGACTACHVGGGTWTCTAAT), and sequenced on an Illumina HiSeq2500 (Illumina, San Diego, CA) using a method developed at the Institute for Genome Sciences [21] and described previously [11, 12].

16S rRNA sequencing analysis

Following sequencing, 16S rRNA paired-end read pairs were assembled using PANDAseq [45], de-multiplexed, trimmed of artificial barcodes and primers, and assessed for chimeras using UCHIME in de novo mode implemented in Quantitative Insights Into Microbial Ecology (QIIME; release v.1.9.1) [7]. Quality trimmed sequences were then clustered de novo into Operational Taxonomic Units (OTUs) and taxonomic assignments were performed using VSEARCH [66] with a minimum confidence threshold of 0.97. The SILVA 16S database [62] in QIIME [7] was used for taxonomic assignments. Downstream data analysis and visualization were completed in RStudio (v.1.1.423) using R packages: biomformat (v.1.2.0) [48] vegan (v.2.4–5) [54], ggplot2 (v.3.1.0) [82], phyloseq (v.1.19.1) [47], and metagenomeSeq (v.1.16.0) [59]. When appropriate, data were normalized with metagenomeSeq’s cumulative sum scaling (CSS) [59] to account for uneven sampling depth. Prior to normalization, alpha diversity was measured using both the Observed richness metric and the Shannon diversity index [69]. Bray-Curtis dissimilarity was used for calculating beta diversity and was compared using analysis of similarities (ANOSIM) on normalized data (999 permutations).

Results

Water quality characteristics

Water quality characteristics of the four sampling sites (non-tidal freshwater creek, reclaimed water, tidal brackish river and freshwater pond) are shown in Table 1. Overall, ambient temperatures, irrespective of sampling site, increased from May to September 2018. Conductivity, nitrate and chloride levels were higher in the tidal brackish creek compared to the other sampling sites, and pH ranged from slightly acidic to slightly basic across all water sample types. Dissolved oxygen was higher in the freshwater pond compared to all other sampling sites.

Table 1 Water quality characteristic averages by sampling site, throughout the sampling period

Culture data

After 3 rounds of isolation and purification, 87 sucrose-positive (yellow) and 28 sucrose-negative (green) purified colonies were obtained from Thiosulfate-citrate-bile salts-sucrose (TCBS) agar resulting in a total of 115 presumptive Vibrio isolates from the four sites during the entire sampling period. Of the 115 presumptive Vibrio isolates, 29 (25.22%) isolates were confirmed via multiplex PCR as vibrios: 17 (14.8%) were confirmed as V. cholerae; 11 (9.6%) were confirmed as V. parahaemolyticus; and 1 (0.87%) was confirmed as V. vulnificus. The V. cholerae isolates were predominantly obtained from tidal brackish water (13 isolates), followed by non-tidal freshwater (2 isolates) and reclaimed water (2 isolate). V. parahaemolyticus isolates were also predominantly obtained from tidal brackish water (9 isolates), and two isolates were obtained from reclaimed water. The one V. vulnificus isolate was recovered from reclaimed water.

Table 2 describes the observed prevalence of confirmed Vibrio spp. by sample type and enrichment media. APW and EPW were similarly effective at detecting Vibrio spp. in reclaimed water samples. However, APW seemed to be more effective than EPW in the detection of V. parahaemolyticus and V. cholerae in tidal brackish water samples.

Table 2 Prevalence of V. parahaemolyticus, V. cholerae and V. vulnificus in nontraditional irrigation water samples processed using culture methods with two different types of enrichment media

16S rRNA sequencing dataset

Extracted DNA from a total of 180 sub-samples as described in Table 3 was PCR- amplified for the 16S rRNA gene and sequenced using the Illumina HiSeq platform. 6,302,683 sequences were generated in total across all samples, and clustered into 17,237 operational taxonomic units (OTUs). Across all samples, the minimum number of reads was 357 and the maximum was 99,944, with an average number of sequences per sample of 35,014.91 (+/− 14,897.3 SD). A Goods estimate coverage of 0.90 was calculated for all samples. Three control samples that were not enriched (1 reclaimed water, 1 pond water and 1 non-tidal freshwater creek sample) had a Good’s estimate coverage < 0.90 and were, therefore, removed to ensure appropriate read coverage across all samples analyzed downstream (Supplementary Figure S1). After data cleanup (removing OTUs with less than 10 reads), the total number of sequences used in downstream analyses was 6,020,192 from 177 samples (n = 47 pond water, n = 47 reclaimed water, n = 35 non-tidal fresh water creek and n = 48 tidal brackish water samples), clustered into 7298 OTUs with a minimum number of 2901 reads and a maximum of 99,857 reads.

Table 3 Summary of total sub-samples tested using our coupled labeling and sequencing approach by water type, BrdU treatment and enrichment method

Alpha and beta diversity

Alpha diversity metrics (Shannon diversity) were calculated on both rarefied (after down-sampling each sample to 2901) and non-rarefied data (data not shown) to avoid sequence coverage issues. Since no differences were observed between the rarefied and non-rarefied analysis, we only presented alpha-diversity analysis performed on the rarefied dataset in Fig. 1A. Irrespective of sampling site/water type, the alpha diversity of BrdU-treated samples was significantly lower (p < 0.001) when compared to non-BrdU treated samples (Fig. 1A).

Fig. 1
figure1

A Box plot of alpha diversity (Shannon Index) across all samples on rarefied data to minimum sampling depth. Alpha diversity of BrdU-treated samples represents the diversity observed in the metabolically-active fraction of bacterial communities present in each sample. Pink represents BrdU-treated samples and yellow represents non-BrdU treated samples. B PCoA analysis of Bray Curtis computed distances between BrdU- and non-BrdU-treated water samples. Solid colored ellipses are drawn at 95% confidence intervals by treatment type of the water samples

Principal coordinate analysis using Bray Curtis distances was implemented to quantify the inter-sample diversity (beta diversity). The analysis revealed that bacterial profiles associated with BrdU-treated samples were similar to the non-BrdU treated samples and showed slight variation by treatment (ANOSIM, R value =0.2241, p = 0.001) (Fig. 1B).

Taxonomic analysis

The top five bacterial phyla identified across all sampling sites irrespective of treatments and enrichments were Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria. The most predominant phyla with an average relative abundance of 44.55% (+/− 0.21) was Proteobacteria, followed by Firmicutes that had an average relative abundance of 24.40% (+/− 0.26). Bacteroidetes, Actinobacteria and Fusobacteria had an average relative abundance of 15.58% (+/− 0.18), 11.45% (+/− 0.13) and 0.96% (+/− 0.04) respectively.

In total, 2205 (30%) OTUs were assigned to the genus level of which only 351 (5%) could be identified to the species level. The top 25 bacterial taxa across all sampling sites, enrichments and treatments were Clostridium bifermentans, unclassified Aeromonadaceae, Pseudomonas, Bacillus cereus, Flavobacterum succinicans, Citrobacter, unclassified ACK-M1, Flavobacterium, unclassified Actinomycetales, Lysinibacillus boronitolerans, unclassified Enterobacteriaceae, Serratia, unclassified Cytophagaceae, Rummeliibacillus, Clostridium metallolevans, Rhodobacter, unclassified C111, Exiguobacterium, Fluviicola, Novosphingobium, Plesiomonas shigelloides, unclassified Chitinophagaceae, unclassified Microbacteriaceae, unclassified C39 and Vibrio (Fig. 2).

Fig. 2
figure2

Taxonomic profiles of the top 25 bacteria detected in pond water, tidal brackish water, reclaimed water and non-tidal freshwater derived from 16S rRNA sequencing data. Pink represents BrdU-treated samples and yellow represents non-BrdU treated samples

Differential abundance analysis was performed to identify bacterial genera that were significantly different (p < 0.05) between enrichments (APW versus no enrichment and EPW versus no enrichment) in all BrdU treated samples (Fig. 3). Vibrio cholerae, Vibrio vulnificus, Clostridium metallolevans, L. boronitolerans, F. succinicans, Enterobacter cloacae, Cetobacterium somerae, B. cereus, P. shigelloides and C. bifermentans were found at a significantly higher abundance in BrdU-treated, non-enriched samples. Additionally, all BrdU-treated enriched (APW and EPW) samples were characterized by a higher relative abundance of Candidatus Aquiluna rubra (Actinobacteria).

Fig. 3
figure3

Differential abundances of bacterial genera that were statistically significant (p < 0.05) in BrdU treated samples between enrichments: no enrichment versus alkaline peptone water (APW), and no enrichment versus estuarine peptone water (EPW). A positive log2-fold change denotes a bacterial taxonomy that is significantly higher in either enrichments (APW or EPW), while a negative log2-fold change indicates a bacterial taxonomy that is significantly higher in no enrichment BrdU-treated samples. The grey line and arrows highlight the conversion in log2-fold change from negative to positive values

Vibrio taxonomy

Irrespective of sampling site, treatments and enrichments, we were able to observe vibrios in the 16S rRNA sequencing data of all samples at a low relative abundance (Fig. 4). Some of the species observed were V. cholerae, V. vulnificus, V. parahaemolyticus, V. aestaurinus and V. shilonii. Among the sampling sites, tidal brackish water samples were characterized by the highest relative abundance of vibrios, followed by reclaimed water samples. Additionally, in the non-enriched BrdU-treated tidal brackish water samples, we observed Vibrio spp., indicating the detection of metabolically-active, viable vibrios, including V. vulnificus, without the aid of enrichment techniques. In non-tidal freshwater, reclaimed water and pond water samples, a higher relative abundance of metabolically-active vibrios coincided with the use of enrichment techniques.

Fig. 4
figure4

Average relative abundance of Vibrio species in pond water, tidal brackish water, reclaimed water and non-tidal freshwater in different enrichments (APW, EPW and no enrichment). Vibrio species abbreviation: VS = V. shilonii, VP = V. parahaemolyticus, VA = V. aestaurinus, VC = V. cholerae, UV = Unclassified Vibrio and VV- V. vulnificus

Additionally, we observed several bacterial taxa that were correlated with the presence of Vibrio (Supplementary Fig. S3). The taxa observed when Vibrio were present in the water samples were Uncl. Pseudomonas, Uncl. Microbacteriaceae, Uncl. c111, Plesiomonas shigelloides, Flavobacterium succinicans, Clostridium metallovens and Clostridium bifermentans.

Comparison between culture-based and 16S rRNA Vibrio spp. detection

Table 4 provides a comparison of Vibrio spp. detection using culture-based versus 16S rRNA sequencing approaches with and without BrdU labeling. Interestingly, 16S rRNA sequencing frequently detected V. aesturianus, V. cholerae, V. parahaemolyticus, V. shilonii, and V. vulnificus across multiple water sample types when our culture-based approach was unable to detect these organisms (Table 4). Most importantly, 16S rRNA sequencing of BrdU labeled samples resulted in Vibrio spp. detection even when our culture-based methods resulted in negative samples (Table 4). This suggests that our coupled labeling and sequencing approach was able to detect metabolically-active Vibrio spp. that may have been present in a VBNC state.

Table 4 Detection of Vibrio spp. in nontraditional irrigation water samples using culture-based methods versus 16S rRNA sequencing with and without BrdU labeling

Discussion

The nontraditional irrigation water sources tested in our study harbored diverse bacterial communities (some of which are of concern to public health), and hence, these water sources would likely require treatment prior to their use for food crop irrigation. Specifically, we were able to detect the presence of Vibrio spp. across all sampling sites. Our culture-based methods were only able to detect Vibrio spp. at three of the sampling sites while our coupled BrdU-labeling and 16S rRNA sequencing approach revealed the presence of vibrios at all four sampling sites (Table 4). Most interestingly, our novel approach of coupling BrdU labeling with 16S rRNA sequencing was able to detect metabolically-active vibrios in water samples that were negative based on our culture results, suggesting that our approach can detect metabolically-active Vibrio spp. that may be present in a VBNC state. We also could detect metabolically-active vibrios in non-enriched samples from all sampling sites, indicating that our coupled method could be helpful in quickly detecting VBNC vibrios without the use of a time-consuming enrichment step.

The bacterial genus Vibrio is ubiquitous and widely distributed in aquatic environments from brackish water to deep seawater, worldwide [19]. These bacteria have also been found in different surface waters [37, 52, 65] and reclaimed water [25, 52, 53]. Most Vibrio-associated illnesses have been associated with either foodborne infections caused by the consumption of raw or undercooked seafood or wound infections acquired during aquatic activities in coastal or estuarine waters [19]. Very rarely, instances of Vibrio outbreaks associated with the consumption of raw vegetables have been reported [9, 28, 63, 76]. For instance Vibrio contamination of vegetables irrigated with partially-treated municipal wastewater in Varanasi, India was reported [63]. Additionally, V. cholerae O1 was detected in vegetables that were irrigated using wastewater and stabilization ponds in Tanzania [28]. The prevalence of V. parahaemolyticus in raw salad vegetables at the retail level was also observed in Malaysia [76]. Nevertheless, to date no Vibrio outbreaks in the U.S. have been associated with the consumption of fresh produce.

Traditionally, pre-enrichment assays (alkaline peptone water) prior to culturing on selective media (thiosulfate-citrate-bile salts-sucrose agar) have been commonly used to improve the detection of pathogenic vibrios from environmental sources [31]. However, these methods are labor intensive, costly and fail to capture VBNC vibrios, thereby resulting in an underestimation of the prevalence of vibrios in the environment. With advances in molecular detection methods (e.g. multiplex Vibrio-specific PCRs) there has been an increased frequency in the detection of vibrios [38]. Yet, viability of the detected organisms is questionable as molecular methods rely on the detection of DNA that can persist in the environment long after bacteria have died. To address this issue, previous studies have employed DNA intercalating dyes such as ethidium monoazide (EMA) and propidium monoazide (PMA) to estimate the total number of viable cells in environmental samples [5, 6, 41]. Recently, Cao et al. [6] was able to detect VBNC V. parahaemolyticus in shrimp samples by utilizing PMA dyes [6]. Though this intercalating dye looks promising and is being widely used to detect metabolically-active bacteria, a recent study by Li et al. [40] found that upon comparing DNA-, PMA- and RNA-based 16S rRNA sequencing, the PMA-based approaches tend to overestimate the live or metabolically-active bacterial population when compared to RNA-based methods.

Prior to the present study, there has been no study using BrdU in tandem with sequencing to detect metabolically-active environmental vibrios, but several studies have used BrdU to detect other metabolically-active bacteria in different environmental samples [44, 46, 75], and improve understanding of cell proliferation with regard to adult neurogenesis [30, 74]. Additionally, BrdU coupled with qPCR has been extensively used to detect persistent fecal bacteria in sewage effluent [79], psychrotolerant bacteria in polluted sea sediments [20] and the impact of mycorrhizal fungi on bacterial communities in soil [2]. Recently, our group coupled BrdU with sequencing techniques to detect metabolically-active bacterial communities in pond and reclaimed water in the mid-Atlantic region [44]. In the present study, our culture-based method was unable to detect Vibrio spp. in any pond water samples, while our 16S rRNA sequencing data (generated from both BrdU treated and non-treated samples) identified V. cholerae, V. parahaemolyticus and V. vulnificus in enriched and non-enriched pond samples (Table 4), indicating the detection of likely VBNC vibrios that the culture-based method could not detect. Also, in all non-enriched BrdU-treated water samples we were able to detect Vibrio spp. (Table 4, Fig. 4), suggesting that our coupled BrdU-labeling and sequencing approach could potentially replace laborious enrichment approaches. Moreover, we were able to detect the presence of other Vibrio’s including V. shilloni (coral pathogen) [39] and V. aesturianus (oyster pathogen) [42] using our coupled labeling and sequencing approach when our culture-based approach was unable to detect these species (Table 4).

Besides Vibrio spp., our findings also revealed the presence of other human bacterial pathogens in BrdU-treated enriched samples including C. bifermentans, B. cereus, P. shigelloides, and E. cloacae (Fig. 2). C. bifermentans is a Gram-positive bacillus known to occur in water, soil, sewage [51], sludge and animal feces [80] and is a rare human pathogen. Unlike other Clostridium species like C. botulinum and C. perfringes (common causes of foodborne illness) [22, 24], C. bifermentans infections have been reported rarely (14 cases to date), none of which have been associated with food-borne illness [26].

In contrast, B. cereus, another Gram-positive, aerobic-to-facultative, spore-forming rod that is widespread in nature, has been frequently isolated from soil and growing plants [4] and has been associated with food-associated illness [8, 72]. Outbreaks of B. cereus have been reported as a result of consumption of contaminated vegetable sprouts [61] and refried beans served at a fast food restaurant chain in upstate New York [8]. In addition, [78] characterized B. cereus isolates from nearly 56 samples of fresh vegetables (peppers, cucumbers, tomatoes, carrots, zucchini, garlic and onions) and in refrigerated, minimally processed foods that included these vegetables as the ingredients [78]. The presence of these organisms in refrigerated, minimally processed foods demonstrates their persistence through food processing methods.

With regard to P. shigelloides, a total of 11 outbreaks have been reported worldwide from 1961 to 2003, of which four outbreaks occurred in the US [34]. Sources of these outbreaks were mainly contaminated shellfish, fish, meat products, and contaminated water sources (tap, well and freshwater) [34]. The common environmental reservoirs for these bacteria include freshwater ecosystems and estuaries [34]. Indirect contamination with P. shigelloides after major natural aquatic disasters has also been reported [68]. For instance, after the 2004 tsunami episode in India, P. shigelloides, along with pathogenic vibrios, Aeromonas and Plesiomonas, were isolated from hand pumps and wells in several communities [35].

E. cloacae, another pathogen detected using our coupled BrdU labeling and sequencing method, has been reported as an opportunistic and multi-drug resistant bacterial pathogen involved in hospital associated outbreaks between 1993 and 2003 in Europe [18]. E. cloacae is ubiquitous in terrestrial and aquatic environments and occurs as a commensal in the intestines of humans and animals, making it a perfect candidate for transfer from irrigated produce to humans. E. cloacae have been isolated from ready-to-eat salads served in a primary school in Valencia city [23] and from vegetables irrigated with untreated wastewater in Morocco [33].

Strengths of this study include the sample size, diversity of water sample types, thorough statistical analysis of our data, our ability to have a head-to-head comparison between traditional culture-based methods and our coupled labeling/sequencing method, and the ability to ultimately differentiate between live/metabolically-active and relic/dead bacterial communities using our novel method. Additionally, we observed the co-occurrence of other bacterial taxa when Vibrio were present in the tested water samples, indicating that these taxa could potentially serve as proxies for Vibrio presence in the environment. This is an interesting area for future research.

Like all 16S rRNA-based sequencing techniques, our study limitations include PCR amplification biases, limited ability to assign species-level classifications (limitations of the currently-available databases), and limited ability to distinguish various Vibrio species among the Vibrionaceae family (due to similar 16S rRNA sequences among the different species). In terms of the BrdU labeling method, it is possible that the 24 h incubation period that we utilized (for both BrdU-labeled and non-labeled samples) may have influenced the bacterial communities observed in our study; however, any changes would have occurred in both BrdU-labeled and non-labeled samples similarly.

Despite these limitations, our findings demonstrate that coupling BrdU-labeling with 16S rRNA sequencing enables the detection of metabolically-active Vibrio spp. that may be present in water samples in a VBNC state. In addition, we showed that our coupled labeling and sequencing approach can detect vibrios in non-enriched BrdU-labeled samples, indicating that the use of this method could replace laborious enrichment steps, shortening time to detection. Finally, our novel method could be used in the future to quickly detect other food- and waterborne pathogens, including Salmonella and Campylobacter, that may be present in a VBNC state and can be difficult to detect using conventional culture-based methods.

Availability of data and materials

The datasets generated and/or analyzed during the current study are deposited in the NCBI BioProject database under BioProject accession number PRJNA473136.

References

  1. 1.

    Alifano P, Bruni CB, Carlomagno MS. Control of mRNA processing and decay in prokaryotes. Genetica. 1994;94(2-3):157–72. https://doi.org/10.1007/BF01443430.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Artursson V, Finlay RD, Jansson JK. Combined bromodeoxyuridine immunocapture and terminal-restriction fragment length polymorphism analysis highlights differences in the active soil bacterial metagenome due to Glomus mosseae inoculation or plant species: active soil bacterial metagenome. Environ Microbiol. 2005;7(12):1952–66. https://doi.org/10.1111/j.1462-2920.2005.00868.x.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Barer MR, Harwood CR. Bacterial viability and Culturability. In: Poole RK, editor. Advances in Microbial Physiology: Academic Press; 1999. p. 93–137.

  4. 4.

    Bottone EJ. Bacillus cereus, a volatile human pathogen. Clin Microbiol Rev. 2010;23(2):382–98. https://doi.org/10.1128/CMR.00073-09.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Brauge T, Midelet-Bourdin G, Soumet C. Viability detection of foodborne bacterial pathogens in food environment by PMA-qPCR and by microscopic observation. In: Bridier A, editor. Foodborne bacterial pathogens. New York: Springer New York; 2019. p. 117–28. https://doi.org/10.1007/978-1-4939-9000-9_9.

    Chapter  Google Scholar 

  6. 6.

    Cao X, Zhao L, Zhang J, Chen X, Shi L, Fang X, et al. Detection of viable but nonculturable Vibrio parahaemolyticus in shrimp samples using improved real-time PCR and real-time LAMP methods. Food Control. 2019;103:145–52. https://doi.org/10.1016/j.foodcont.2019.04.003.

    CAS  Article  Google Scholar 

  7. 7.

    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6. https://doi.org/10.1038/nmeth.f.303.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Carroll LM, Wiedmann M, Mukherjee M, Nicholas DC, Mingle LA, Dumas NB, et al. Characterization of emetic and diarrheal Bacillus cereus strains from a 2016 foodborne outbreak using whole-genome sequencing: addressing the microbiological, epidemiological, and Bioinformatic challenges. Front Microbiol. 2019a;10. https://doi.org/10.3389/fmicb.2019.00144.

  9. 9.

    Centers for Disease Control and Prevention (CDC). Cholera epidemic associated with raw vegetables--Lusaka, Zambia, 2003-2004. MMWR Morb Mortal Wkly Rep. 2004;53:783–6.

    Google Scholar 

  10. 10.

    Chandrasekharam D. Water for the millions: focus Saudi Arabia. Water-Energy Nexus. 2018;1(2):142–4. https://doi.org/10.1016/j.wen.2019.01.001.

    Article  Google Scholar 

  11. 11.

    Chopyk J, Chattopadhyay S, Kulkarni P, Claye E, Babik KR, Reid MC, et al. Mentholation affects the cigarette microbiota by selecting for bacteria resistant to harsh environmental conditions and selecting against potential bacterial pathogens. Microbiome. 2017a;5(1):22. https://doi.org/10.1186/s40168-017-0235-0.

    Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Chopyk J, Chattopadhyay S, Kulkarni P, Smyth EM, Hittle LE, Paulson JN, et al. Temporal Variations in Cigarette Tobacco Bacterial Community Composition and Tobacco-Specific Nitrosamine Content Are Influenced by Brand and Storage Conditions. Front Microbiol. 2017b;8:358.

    Article  Google Scholar 

  13. 13.

    Colwell RR, Brayton PR, Grimes DJ, Roszak DB, Huq SA, Palmer LM. Viable but non-Culturable Vibrio cholerae and related pathogens in the environment: implications for release of genetically engineered microorganisms. Bio/Technology. 1985;3:817.

    Google Scholar 

  14. 14.

    Colwell RR, Huq A. Environmental reservoir of Vibrio cholerae the causative agent of cholera. Ann N Y Acad Sci. 1994;740(1 Disease in Ev):44–54. https://doi.org/10.1111/j.1749-6632.1994.tb19852.x.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Colwell RR, Kaper J, Joseph SW. Vibrio cholerae, Vibrio parahaemolyticus, and other Vibrios: occurrence and distribution in Chesapeake Bay. Science. 1977;198(4315):394–6. https://doi.org/10.1126/science.910135.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Cristescu ME. Can environmental RNA revolutionize biodiversity science? Trends Ecol Evol. 2019;34(8):694–7. https://doi.org/10.1016/j.tree.2019.05.003.

    Article  PubMed  Google Scholar 

  17. 17.

    Daniels NA, MacKinnon L, Bishop R, Altekruse S, Ray B, Hammond RM, et al. Vibrio parahaemolyticus infections in the United States, 1973–1998. J Infect Dis. 2000;181(5):1661–6. https://doi.org/10.1086/315459.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Davin-Regli A, Pagès, J.-M. Enterobacter aerogenes and Enterobacter cloacae; versatile bacterial pathogens confronting antibiotic treatment. Front Microbiol. 2015;6. https://doi.org/10.3389/fmicb.2015.00392.

  19. 19.

    Doyle, Buchanan. Vibrio Species. In: Food Microbiology: American Society of Microbiology; 2013. p. 401–39.

  20. 20.

    Edlund A, Jansson JK. Use of bromodeoxyuridine immunocapture to identify psychrotolerant phenanthrene-degrading bacteria in phenanthrene-enriched polluted Baltic Sea sediments: enrichment of phenanthrene degraders. FEMS Microbiol Ecol. 2008;65(3):513–25. https://doi.org/10.1111/j.1574-6941.2008.00513.x.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Fadrosh DW, Ma B, Gajer P, Sengamalay N, Ott S, Brotman RM, et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome. 2014;2(1):6. https://doi.org/10.1186/2049-2618-2-6.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Fafangel M, Učakar V, Vudrag M, Berce I, Kraigher A. A five site Clostridium perfringens food-borne outbreak: a retrospective cohort study / Izbruh Okužb S Clostridium perfringens, Prenesenih S Hrano, Na Petih Lokacijah: Retrospektivna Kohortna Raziskava. Slovenian J Public Health. 2015;54(1):51–7. https://doi.org/10.1515/sjph-2015-0007.

    Article  Google Scholar 

  23. 23.

    Falomir MP, Gozalbo D, Rico H. Coliform bacteria in fresh vegetables: from cultivated lands to consumers; 2010. p. 7.

    Google Scholar 

  24. 24.

    Fleck-Derderian S, Shankar M, Rao AK, Chatham-Stephens K, Adjei S, Sobel J, et al. The epidemiology of foodborne botulism outbreaks: a systematic review. Clin Infect Dis. 2018;66(suppl_1):S73–81. https://doi.org/10.1093/cid/cix846.

    CAS  Article  Google Scholar 

  25. 25.

    Gurbanov S, Akhmadov R, Shamkhalova G, Akhmadova S, Haley BJ, Colwell RR, et al. Occurrence of Vibrio cholerae in municipal and natural waters and incidence of cholera in Azerbaijan. EcoHealth. 2011;8(4):468–77. https://doi.org/10.1007/s10393-012-0756-8.

    Article  PubMed  Google Scholar 

  26. 26.

    Hale A, Kirby JE, Albrecht M. Fatal spontaneous Clostridium bifermentans necrotizing endometritis: a case report and literature review of the pathogen. Open Forum Infect Dis. 2016;3:ofw095.

    Article  Google Scholar 

  27. 27.

    Hanin M, Ebel C, Ngom M, Laplaze L, Masmoudi K. New insights on plant salt tolerance mechanisms and their potential use for breeding. Front Plant Sci. 2016;7. https://doi.org/10.3389/fpls.2016.01787.

  28. 28.

    Hounmanou YMG, Mdegela RH, Dougnon TV, Mhongole OJ, Mayila ES, Malakalinga J, et al. Toxigenic Vibrio cholerae O1 in vegetables and fish raised in wastewater irrigated fields and stabilization ponds during a non-cholera outbreak period in Morogoro, Tanzania: an environmental health study. BMC Res Notes. 2016;9(1):466. https://doi.org/10.1186/s13104-016-2283-0.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Huang M, Zhang Z, Zhu C, Zhai Y, Lu P. Effect of biochar on sweet corn and soil salinity under conjunctive irrigation with brackish water in coastal saline soil. Sci Hortic. 2019a;250:405–13. https://doi.org/10.1016/j.scienta.2019.02.077.

    CAS  Article  Google Scholar 

  30. 30.

    Huang X, Liu J, Wu W, Hu P, Wang Q. Taurine enhances mouse cochlear neural stem cell transplantation via the cochlear lateral wall for replacement of degenerated spiral ganglion neurons via sonic hedgehog signaling pathway. Cell Tissue Res. 2019b;378(1):49–57. https://doi.org/10.1007/s00441-019-03018-6.

    Article  PubMed  Google Scholar 

  31. 31.

    Huq A, Haley BJ, Taviani E, Chen A, Hasan NA, Colwell RR. Detection, isolation, and identification of Vibrio cholerae from the environment. Curr Protocols Microbiol. 2012;26(1). https://doi.org/10.1002/9780471729259.mc06a05s26.

  32. 32.

    Hussain SA, Farooq MA, Akhtar J, Saqib ZA. Silicon-mediated growth and yield improvement of sunflower (Helianthus annus L.) subjected to brackish water stress. Acta Physiol Plant. 2018;40:180.

    Article  Google Scholar 

  33. 33.

    Ibenyassine K, Mhand RA, Karamoko Y, Anajjar B, Chouibani MM, Ennaji M. Bacterial pathogens recovered from vegetables irrigated by wastewater in Morocco. J Environ Health. 2007;69(10):47–51.

    CAS  PubMed  Google Scholar 

  34. 34.

    Janda JM, Abbott SL, McIver CJ. Plesiomonas shigelloides revisited. Clin Microbiol Rev. 2016;29(2):349–74. https://doi.org/10.1128/CMR.00103-15.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Kanungo R, Shashikala, Karunasagar I, Srinivasan S, Sheela D, Venkatesh K, et al. Contamination of community water sources by potentially pathogenic vibrios following sea water inundation. J Commun Dis. 2007;39(4):229–32.

    PubMed  Google Scholar 

  36. 36.

    Kell DB, Kaprelyants AS, Weichart DH, Harwood CR, Barer MR. Viability and activity in readily culturable bacteria: a review and discussion of the practical issues. Antonie Van Leeuwenhoek. 1998;73(2):169–87. https://doi.org/10.1023/A:1000664013047.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Khan MU, Shahidullah MD, Haque MS, Ahmed WU. Presence of vibrios in surface water and their relation with cholera in a community. Trop Geogr Med. 1984;36(4):335–40.

    CAS  PubMed  Google Scholar 

  38. 38.

    Kim H-J, Ryu J-O, Lee S-Y, Kim E-S, Kim H-Y. Multiplex PCR for detection of the Vibrio genus and five pathogenic Vibrio species with primer sets designed using comparative genomics. BMC Microbiol. 2015;15:239.

    Article  Google Scholar 

  39. 39.

    Kushmaro A, Banin E, Loya Y, Stackebrandt E, Rosenberg E. Vibrio shiloi sp. nov., the causative agent of bleaching of the coral Oculina patagonica. Int J Syst Evol Microbiol. 2001;51(4):1383–8. https://doi.org/10.1099/00207713-51-4-1383.

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Li R, Tun HM, Jahan M, Zhang Z, Kumar A, Dilantha Fernando WG, et al. Comparison of DNA-, PMA-, and RNA-based 16S rRNA Illumina sequencing for detection of live bacteria in water. Sci Rep. 2017;7(1):5752. https://doi.org/10.1038/s41598-017-02516-3.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Liu Y, Zhong Q, Wang J, Lei S. Enumeration of Vibrio parahaemolyticus in VBNC state by PMA-combined real-time quantitative PCR coupled with confirmation of respiratory activity. Food Control. 2018;91:85–91. https://doi.org/10.1016/j.foodcont.2018.03.037.

    CAS  Article  Google Scholar 

  42. 42.

    Lupo C, Travers M-A, Tourbiez D, Barthélémy CF, Beaunée G, Ezanno P. Modeling the transmission of Vibrio aestuarianus in Pacific oysters using experimental infection data. Front Vet Sci. 2019;6:142. https://doi.org/10.3389/fvets.2019.00142.

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Ma K, Deng Y, Bai Y, Xu D, Chen E, Wu H, et al. Rapid and simultaneous detection of Salmonella, Shigella, and Staphylococcus aureus in fresh pork using a multiplex real-time PCR assay based on immunomagnetic separation. Food Control. 2014;42:87–93. https://doi.org/10.1016/j.foodcont.2014.01.042.

    CAS  Article  Google Scholar 

  44. 44.

    Malayil L, Ramachandran P, Chattopadhyay S, Cagle R, Hittle L, Ottesen A, et al. Metabolically-active bacteria in reclaimed water and ponds revealed using bromodeoxyuridine DNA labeling coupled with 16S rRNA and shotgun sequencing. Water Res. 2020;184:116185. https://doi.org/10.1016/j.watres.2020.116185.

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics. 2012;13(1):31. https://doi.org/10.1186/1471-2105-13-31.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    McMahon SK, Wallenstein MD, Schimel JP. Microbial growth in Arctic tundra soil at −2°C. Environ Microbiol Rep. 2009;1(2):162–6. https://doi.org/10.1111/j.1758-2229.2009.00025.x.

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.

    CAS  Article  Google Scholar 

  48. 48.

    McMurdie PJ, Paulson JN. Biomformat: an interface package for the BIOM file format; 2017.

    Google Scholar 

  49. 49.

    Mizunoe Y, Wai SN, Ishikawa T, Takade A, Yoshida S. Resuscitation of viable but nonculturable cells of Vibrio parahaemolyticus induced at low temperature under starvation. FEMS Microbiol Lett. 2000;186(1):115–20. https://doi.org/10.1111/j.1574-6968.2000.tb09091.x.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Munns R, Tester M. Mechanisms of salinity tolerance. Annu Rev Plant Biol. 2008;59(1):651–81. https://doi.org/10.1146/annurev.arplant.59.032607.092911.

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Nachman S, Kaul A, Li KI, Slim MS, San Filippo JA, Van Horn K. Liver abscess caused by Clostridium bifermentans following blunt abdominal trauma. J Clin Microbiol. 1989;27(5):1137–8. https://doi.org/10.1128/JCM.27.5.1137-1138.1989.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Nongogo V, Okoh A. Occurrence of Vibrio Pathotypes in the final effluents of five wastewater treatment plants in Amathole and Chris Hani District municipalities in South Africa. Int J Environ Res Public Health. 2014;11(8):7755–66. https://doi.org/10.3390/ijerph110807755.

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Okoh AI, Sibanda T, Nongogo V, Adefisoye M, Olayemi OO, Nontongana N. Prevalence and characterisation of non-cholerae Vibrio spp. in final effluents of wastewater treatment facilities in two districts of the eastern Cape Province of South Africa: implications for public health. Environ Sci Pollut Res. 2015;22(3):2008–17. https://doi.org/10.1007/s11356-014-3461-z.

    CAS  Article  Google Scholar 

  54. 54.

    Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., et al. (2017) Vegan: community ecology package.

    Google Scholar 

  55. 55.

    Oliver JD. The public health significance of viable but Nonculturable Bacteria. In: Nonculturable Microorganisms in the Environment. Boston: Springer; 2000. p. 277–300.

    Chapter  Google Scholar 

  56. 56.

    Oliver JD. Wound infections caused by Vibrio vulnificus and other marine bacteria. Epidemiol Infect. 2005;133:383–91.

    CAS  Article  Google Scholar 

  57. 57.

    Patel RM, Prasher SO, Donnelly D, Bonnell RB, Broughton RS. Subirrigation with brackish water for vegetable production in arid regions. Bioresour Technol. 1999;70(1):33–7. https://doi.org/10.1016/S0960-8524(99)00012-7.

    CAS  Article  Google Scholar 

  58. 58.

    Patel RM, Prasher SO, Goel PK, Madramootoo CA, Broughton RS. Brackish water subirrigation for vegetables. Irrig Drain. 2003;52(2):121–32. https://doi.org/10.1002/ird.70.

    Article  Google Scholar 

  59. 59.

    Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10(12):1200–2. https://doi.org/10.1038/nmeth.2658.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Pochon X, Zaiko A, Fletcher LM, Laroche O, Wood SA. Wanted dead or alive? Using metabarcoding of environmental DNA and RNA to distinguish living assemblages for biosecurity applications. PLoS One. 2017;12(11):e0187636. https://doi.org/10.1371/journal.pone.0187636.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Portnoy BL, Goepfert JM, Harmon SM. An outbreak of Bacillus cereus food poisoning resulting from contaminated vegetable sprouts. Am J Epidemiol. 1976;103(6):589–94. https://doi.org/10.1093/oxfordjournals.aje.a112263.

    CAS  Article  PubMed  Google Scholar 

  62. 62.

    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41(D1):D590–6. https://doi.org/10.1093/nar/gks1219.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Rai PK, Tripathi BD. Microbial contamination in vegetables due to irrigation with partially treated municipal wastewater in a tropical city. Int J Environ Health Res. 2007;17(5):389–95. https://doi.org/10.1080/09603120701628743.

    Article  PubMed  Google Scholar 

  64. 64.

    Rashed AA. Vegetable production on Rice bales using brackish treated drainage water. Irrig Drain. 2016;65(5):664–72. https://doi.org/10.1002/ird.1994.

    Article  Google Scholar 

  65. 65.

    Rhodes JB, Smith HL, Ogg JE. Isolation of non-Ol Vibrio cholerae Serovars from surface waters in Western Colorado. Appl Environ Microbiol. 1986;51:4.

    Article  Google Scholar 

  66. 66.

    Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. https://doi.org/10.7717/peerj.2584.

    Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Sapkota AR. Water reuse, food production and public health: adopting transdisciplinary, systems-based approaches to achieve water and food security in a changing climate. Environ Res. 2019;171:576–80. https://doi.org/10.1016/j.envres.2018.11.003.

    CAS  Article  PubMed  Google Scholar 

  68. 68.

    Schuetz AN. Emerging agents of gastroenteritis: Aeromonas, Plesiomonas, and the diarrheagenic pathotypes of Escherichia coli. Semin Diagn Pathol. 2019;36(3):187–92. https://doi.org/10.1053/j.semdp.2019.04.012.

    Article  PubMed  Google Scholar 

  69. 69.

    Shannon CE. A mathematical theory of communication. Bell System Tech J. 1948;27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.

    Article  Google Scholar 

  70. 70.

    Shrivastava P, Kumar R. Soil salinity: a serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi J Biol Sci. 2015;22(2):123–31. https://doi.org/10.1016/j.sjbs.2014.12.001.

    CAS  Article  PubMed  Google Scholar 

  71. 71.

    Stanton JS. Brackish groundwater in the United States. Reston: U.S. Department of the Interior, U.S. Geological Survey; 2017.

    Book  Google Scholar 

  72. 72.

    Stenfors Arnesen LP, Fagerlund A, Granum PE. From soil to gut: Bacillus cereus and its food poisoning toxins. FEMS Microbiol Rev. 2008;32(4):579–606. https://doi.org/10.1111/j.1574-6976.2008.00112.x.

    CAS  Article  PubMed  Google Scholar 

  73. 73.

    Tan SC, Yiap BC. DNA, RNA, and protein extraction: the past and the present. J Biomed Biotechnol. 2009;2009:574398.

    Article  Google Scholar 

  74. 74.

    Taupin P. BrdU immunohistochemistry for studying adult neurogenesis: paradigms, pitfalls, limitations, and validation. Brain Res Rev. 2007;53(1):198–214. https://doi.org/10.1016/j.brainresrev.2006.08.002.

    CAS  Article  PubMed  Google Scholar 

  75. 75.

    Torsvik V, Øvreås L. Microbial diversity and function in soil: from genes to ecosystems. Curr Opin Microbiol. 2002;5(3):240–5. https://doi.org/10.1016/S1369-5274(02)00324-7.

    CAS  Article  PubMed  Google Scholar 

  76. 76.

    Tunung R, Margaret S, Jeyaletchumi P, Chai LC, Tuan Zainazor TC, Ghazali FM, et al. Prevalence and quantification of Vibrio parahaemolyticus in raw salad vegetables at retail level. J Microbiol Biotechnol. 2010;20(2):391–6. https://doi.org/10.4014/jmb.0908.08009.

    CAS  Article  PubMed  Google Scholar 

  77. 77.

    Urbach E, Vergin KL, Giovannoni SJ. Immunochemical detection and isolation of DNA from metabolically active bacteria. Appl Environ Microbiol. 1999;65(3):1207–13. https://doi.org/10.1128/AEM.65.3.1207-1213.1999.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Valero M, Hernández-Herrero LA, Fernández PS, Salmerón MC. Characterization of Bacillus cereus isolates from fresh vegetables and refrigerated minimally processed foods by biochemical and physiological tests. Food Microbiol. 2002;19(5):491–9. https://doi.org/10.1006/fmic.2002.0507.

    CAS  Article  Google Scholar 

  79. 79.

    Walters SP, Field KG. Persistence and growth of fecal Bacteroidales assessed by Bromodeoxyuridine Immunocapture. Appl Environ Microbiol. 2006;72(7):4532–9. https://doi.org/10.1128/AEM.00038-06.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Wang CC, Chang CW, Chu CP, Lee DJ, Chang B-V, Liao CS. Producing hydrogen from wastewater sludge by Clostridium bifermentans. J Biotechnol. 2003;102(1):83–92. https://doi.org/10.1016/S0168-1656(03)00007-5.

    CAS  Article  PubMed  Google Scholar 

  81. 81.

    Westrich JR, Griffin DW, Westphal DL, Lipp EK. Vibrio population dynamics in mid-Atlantic surface waters during Saharan dust events. Front Mar Sci. 2018;5. https://doi.org/10.3389/fmars.2018.00012.

  82. 82.

    Wickham H. ggplot2. New York: Springer New York; 2009.

    Book  Google Scholar 

  83. 83.

    Wright AC, Hill RT, Johnson JA, Roghman MC, Colwell RR, Morris JG. Distribution of Vibrio vulnificus in the Chesapeake Bay. Appl Environ Microbiol. 1996;62(2):717–24. https://doi.org/10.1128/AEM.62.2.717-724.1996.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Xu Y-G, Liu Z-M, Zhang B-Q, Qu M, Mo C-S, Luo J, et al. Development of a novel target-enriched multiplex PCR (tem-PCR) assay for simultaneous detection of five foodborne pathogens. Food Control. 2016;64:54–9. https://doi.org/10.1016/j.foodcont.2015.12.022.

    CAS  Article  Google Scholar 

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Acknowledgements

We would like to thank the CONSERVE Co-Project Directors and the Mid-Atlantic CONSERVE core sampling team, including Manan Sharma, Kali Kniel, Shirley Micallef, Fawzy Hashem, Chuck Gerba, Sadhana Ravishankar, Salina Parveen, Eric May, Amir Sapkota,

Mihai Pop, Sarah Allard, Anthony Bui, Mary Theresa Callahan, Hillary Craddock Kelbick, Rianna Murray, Cheryl East, Eric Handy, Prachi Kulkarni, Brienna Anderson, Shani Craighead, Samantha Gartley, Adam Vanore, Rico Duncan, Derek Foust and Joseph Haymaker. Additionally, we would like to thank Padmini Ramachandran for help with data analysis.

Funding

This work was supported by the United States Department of Agriculture-National Institute of Food and Agriculture, Grant number 2016–68007-25064, awarded to the University of Maryland School of Public Health, that established CONSERVE: A Center of Excellence at the Nexus of Sustainable Water Reuse, Food and Health. SC and ARS were supported by NRT-INFEWS: UMD Global STEWARDS (STEM Training at the Nexus of Energy, WAter Reuse and FooD Systems) that was awarded to the University of Maryland School of Public Health by the National Science Foundation National Research Traineeship Program, Grant number 1828910. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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LM conceived the study design, performed the lab experiments and bioinformatic analyses, wrote and edited the manuscript. SC performed the lab experiments, reviewed and edited the manuscript and approved the final version. EM contributed to the data analysis and interpretation, reviewed and edited the manuscript, and approved the final version. ARS acquired funding, contributed to the lab experiments and data interpretation, reviewed and edited the manuscript, and approved the final version. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Amy R. Sapkota.

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Supplementary Information

Additional file 1: Supplementary Fig. 1.

Good’s coverage among the different water sampling sites.

Additional file 2: Supplementary Fig. 2.

Description of the sample processing and analysis approach.

Additional file 3: Supplementary Fig. 3.

Co-occurrence of bacterial taxa in the presence and absence of Vibrio, visualized by a chord plot.

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Malayil, L., Chattopadhyay, S., Mongodin, E.F. et al. Coupled DNA-labeling and sequencing approach enables the detection of viable-but-non-culturable Vibrio spp. in irrigation water sources in the Chesapeake Bay watershed. Environmental Microbiome 16, 13 (2021). https://doi.org/10.1186/s40793-021-00382-1

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Keywords

  • Viable-but-nonculturable
  • Vibrio
  • DNA-labeling
  • Sequencing
  • Reclaimed water
  • Brackish water
  • BrdU