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. 2021 Jan 15;87(3):e01403-20. doi: 10.1128/AEM.01403-20

Phylogenetic and Biogeographic Patterns of Vibrio parahaemolyticus Strains from North America Inferred from Whole-Genome Sequence Data

John J Miller a,b, Bart C Weimer c, Ruth Timme d, Catharina H M Lüdeke e,f, James B Pettengill a, D J Darwin Bandoy c, Allison M Weis c, James Kaufman g, B Carol Huang c, Justin Payne d, Errol Strain a, Jessica L Jones e,✉
Editor: Johanna Björkrothh
PMCID: PMC7848924  PMID: 33187991

Vibrio parahaemolyticus is the most common cause of seafood-borne illness reported in the United States and is frequently associated with shellfish consumption. This study contributes to our knowledge of the biogeography and functional genomics of this species around North America.

KEYWORDS: Vibrio parahaemolyticus, MLST, phylogenetics, genomics, kSNP, cluster analysis

ABSTRACT

Vibrio parahaemolyticus is the most common cause of seafood-borne illness reported in the United States. The draft genomes of 132 North American clinical and oyster V. parahaemolyticus isolates were sequenced to investigate their phylogenetic and biogeographic relationships. The majority of oyster isolate sequence types (STs) were from a single harvest location; however, four were identified from multiple locations. There was population structure along the Gulf and Atlantic Coasts of North America, with what seemed to be a hub of genetic variability along the Gulf Coast, with some of the same STs occurring along the Atlantic Coast and one shared between the coastal waters of the Gulf and those of Washington State. Phylogenetic analyses found nine well-supported clades. Two clades were composed of isolates from both clinical and oyster sources. Four were composed of isolates entirely from clinical sources, and three were entirely from oyster sources. Each single-source clade consisted of one ST. Some human isolates lack tdh, trh, and some type III secretion system (T3SS) genes, which are established virulence genes of V. parahaemolyticus. Thus, these genes are not essential for pathogenicity. However, isolates in the monophyletic groups from clinical sources were enriched in several categories of genes compared to those from monophyletic groups of oyster isolates. These functional categories include cell signaling, transport, and metabolism. The identification of genes in these functional categories provides a basis for future in-depth pathogenicity investigations of V. parahaemolyticus.

IMPORTANCE Vibrio parahaemolyticus is the most common cause of seafood-borne illness reported in the United States and is frequently associated with shellfish consumption. This study contributes to our knowledge of the biogeography and functional genomics of this species around North America. STs shared between the Gulf Coast and the Atlantic seaboard as well as Pacific waters suggest possible transport via oceanic currents or large shipping vessels. STs frequently isolated from humans but rarely, if ever, isolated from the environment are likely more competitive in the human gut than other STs. This could be due to additional functional capabilities in areas such as cell signaling, transport, and metabolism, which may give these isolates an advantage in novel nutrient-replete environments such as the human gut.

INTRODUCTION

Vibrio parahaemolyticus is a halophilic, Gram-negative bacterium that inhabits coastal estuarine environments (1). In the United States, V. parahaemolyticus is the leading cause of seafood-borne infections (2). The incidence of V. parahaemolyticus infection continues to increase in the United States and globally (3, 4). Consumption of raw or undercooked seafood harboring V. parahaemolyticus can result in mild to acute gastroenteritis, while contact with seawater containing this bacterium occasionally results in wound infections and, rarely, sepsis (5). The pathogenicity of this bacterium has historically been linked to the presence of two hemolysin genes: the thermostable direct hemolysin (tdh) and the tdh-related hemolysin (trh) (1). However, recent studies have shown that some strains produce cytotoxic and enterotoxic effects independent of their hemolysin production (6, 7). Also, some clinical isolates have been reported to lack one or both of the virulence markers tdh and trh, and phylogeny is independent of the genes (8–10). In light of this, type III secretion systems (T3SSs) have been investigated as potential pathogenicity factors for V. parahaemolyticus (11, 12). Among isolates that carry the hemolysin genes, certain serotypes (O3:K6 and O4:K12) and/or sequence types (STs) (ST3 and -36) are thought to be more virulent, as inferred by their illness incidences (13–15). In addition to the apparent divergence in the virulence potential of V. parahaemolyticus based on serotype or ST, there is a strong correlation with isolate/source location, suggesting that geography may play a role (16). For example, the majority of shellfish-associated V. parahaemolyticus illnesses in the United States have been from the Pacific Northwest and the Northeast Atlantic coasts (15, 17) rather than the Gulf Coast, which is also a major producer of commercial shellfish and where V. parahaemolyticus thrives (1, 18–20). Together, these data indicate that there is still much to be learned about the population dynamics and virulence potential of this species.

Previous investigations of V. parahaemolyticus utilizing multilocus sequence typing (MLST), pulsed-field gel electrophoresis (PFGE), and multiple-locus variable-number tandem-repeat analysis (MLVA) have indicated high genetic diversity (9, 21–24). However, the availability of whole-genome sequencing (WGS), more powerful computing, and better algorithms has made phylogenetic analysis based on the data collected from the entire genome possible. Also, genome sequencing has made in silico MLST analysis possible (25, 26). Previous sequencing of V. parahaemolyticus demonstrated substantial genetic similarity between disease-associated clinical and certain environmental isolates, even with relatively limited strain sets (15, 27). This suggests that sequencing additional genomes from diverse clinical and environmental V. parahaemolyticus isolates will provide information for further insight into the ability to transition from an environmental niche to a human pathogen (27).

In this study, we sequenced 132 V. parahaemolyticus genomes that are geographically representative of North America and diverse regarding serotype and ST. The draft genome data were used for phylogenetic analyses, including maximum likelihood analysis and whole-genome distance analysis (WGDA), which have been successfully applied to bacterial genomes, as well as in silico MLST (26, 28, 29). These analyses were used to infer biogeographical and phylogenetic relationships of V. parahaemolyticus as a means to understand the genomic variation within V. parahaemolyticus. In addition, the data were used to further investigate the differential virulence potentials of isolates.

RESULTS

Genome description.

Draft genomes were collected for 132 V. parahaemolyticus isolates from oysters collected from the coasts of North America and patients in the United States. Rarefaction analysis indicated that this population contains all the genes in V. parahaemolyticus as the asymptote occurs at a pangenome size of 8,191 genes, prior to sampling all 132 genomes (Fig. 1). The core genome consisted of 3,726 genes (see Fig. S1 in the supplemental material), with much of the pangenome being composed of genes from fewer than 24 isolates (18%).

FIG 1.

FIG 1

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Genome gene discovery and pangenome. (A) To evaluate the completeness of the gene-finding effort, a rarefaction plot was constructed by listing the COG to which each V. parahaemolyticus isolate belonged, resampling the isolates with replacement using random.randrange in Python 3.6, and then listing and counting the unique COGs. This was done for increasing numbers of isolates. For each number of isolates, 10 replicates were done. The plot was constructed with Plotnine, geom_point. (B) The pangenome was constructed to demonstrate a core genome with a highly variable auxiliary genome.

Population genetics and biogeography.

There were 61 STs identified from the 132 isolates (Table 1), of which 35 were represented by only one isolate and 21 were associated with multiple serotypes. Five isolates could not be assigned sequence types by MLST due to recA or pntA genes that could not be typed. The three most frequent STs were ST36, ST1151, and ST3. Only two sequence types, ST775 and ST34, were collected from both oyster and clinical sources.

TABLE 1.

Metadata of the 132 V. parahaemolyticus shotgun sequences

CFSAN ID Strain ID Source State or provincea Date of isolation (day-mo-yr)b Serotype Sequence type SRA accession no. GenBank assembly ID GenBank accession no. BioSample accession no. Coverage depth (%) Total length (bp) No. of contigs No. of genes
CFSAN011163 CDC_K4556-2 Clinical LA 23-Oct-06 O1:Kuk ST744 SRR1118644 GCA_001727365.1 MISQ00000000 SAMN02368278 74 5,149,094 135 4,735
CFSAN011164 CDC_K4557 Clinical LA 19-Sep-06 O1:K33 ST799 SRR1840765 GCA_001727725.1 MISR00000000 SAMN02368279 35 5,074,019 94 4,619
CFSAN011165 CDC_K4558-1 Clinical LA 28-Aug-06 O3:K39 ST1143 SRR1118643 GCA_001727735.1 MISS00000000 SAMN02368280 34 5,032,007 120 4,611
CFSAN011166 CDC_K4558-2 Clinical LA 28-Aug-06 O3:Kuk ST636 SRR1118642 GCA_001727745.1 MIST00000000 SAMN02368281 88 5,335,587 155 4,897
CFSAN011167 CDC_K4588 Clinical ME 26-Jul-06 O5:Kuk ST746 SRR1118641 GCA_001727775.1 MISU00000000 SAMN02368282 57 5,014,336 118 4,610
CFSAN011168 CDC_K4636 Clinical NY 25-Sep-06 O10:Kuk ST636 SRR1815538 GCA_001727805.1 MISV00000000 SAMN03358827 35 5,340,949 138 4,889
CFSAN011169 CDC_K4637-1 Clinical NY 1-Oct-06 O3:K6 ST3 SRR1815539 GCA_001727815.1 MISW00000000 SAMN03358828 59 5,163,379 150 4,755
CFSAN011170 CDC_K4637-2 Clinical NY 1-Oct-06 O3:K6 ST3 SRR1815540 GCA_001727825.1 MISX00000000 SAMN03358829 59 5,156,414 133 4,740
CFSAN011171 CDC_K4638 Clinical NY 25-Sep-06 O10:Kuk ST809 SRR1118640 GCA_001727845.1 MISY00000000 SAMN02368283 47 5,077,332 96 4,619
CFSAN011172 CDC_K4639-1 Clinical NY 16-Oct-06 O4:K12 ST36 SRR1815541 GCA_001727895.1 MISZ00000000 SAMN03358830 61 5,092,291 122 4,654
CFSAN011173 CDC_K4639-2 Clinical NY 16-Oct-06 O4:Kuk ST36 SRR1118639 GCA_001727885.1 MITA00000000 SAMN02368284 57 5,078,734 114 4,658
CFSAN011175 CDC_K4762 Clinical VA 15-Aug-06 O5:K17 ST674 SRR1118637 GCA_001727925.1 MITB00000000 SAMN02368286 78 5,103,196 108 4,676
CFSAN011176 CDC_K4763 Clinical VA 25-Aug-06 O5:Kuk Untyped SRR1840764 GCA_001727915.1 MITC00000000 SAMN02368287 43 5,287,071 129 4,843
CFSAN011177 CDC_K4764-1 Clinical VA 13-Oct-06 O8:K41 Untyped SRR1815542 GCA_001727965.1 MITE00000000 SAMN03358831 43 5,276,586 114 4,813
CFSAN011178 CDC_K4764-2 Clinical VA 13-Oct-06 O8:K41 ST1156 SRR1118636 GCA_001727975.1 MITD00000000 SAMN02368288 52 5,033,430 111 4,616
CFSAN011179 CDC_K4775 Clinical GA 24-Feb-07 O3:K6 ST3 SRR1118635 GCA_001728005.1 MITF00000000 SAMN02368289 71 5,158,365 121 4,699
CFSAN011180 CDC_K4842 Clinical MD 16-Oct-06 O5:K47 ST1144 SRR1118634 GCA_001727995.1 MITG00000000 SAMN02368290 47 5,194,684 115 4,791
CFSAN011181 CDC_K4857-1 Clinical HI 28-Jan-07 O5:K17 ST79 SRR1118633 GCA_001728045.1 MITH00000000 SAMN02368291 37 5,038,683 103 4,569
CFSAN011182 CDC_K4857-2 Clinical HI 28-Jan-07 O5:Kuk ST79 SRR1118632 GCA_001728065.1 MITI00000000 SAMN02368292 47 5,039,775 108 4,592
CFSAN011183 CDC_K4858 Clinical HI 15-Sep-06 O4:K4 ST283 SRR1840763 GCA_001728085.1 MITJ00000000 SAMN02368293 56 4,961,237 89 4,534
CFSAN011184 CDC_K4859 Clinical HI 15-Feb-07 O6:K18 Untyped SRR1118631 GCA_001728095.1 MITK00000000 SAMN02368294 192 5,180,859 107 4,732
CFSAN011185 CDC_K4981 Clinical OK 12-Mar-07 O1:Kuk ST748 SRR1118630 GCA_001728125.1 MITL00000000 SAMN02368295 118 4,928,549 135 4,500
CFSAN011187 CDC_K5009-2 Clinical MA 7-Aug-06 O4:K53 ST749 SRR1118629 GCA_001728135.1 MITM00000000 SAMN02368297 94 5,125,537 130 4,681
CFSAN011188 CDC_K5010-1 Clinical MA 16-Sep-06 O1:Kuk ST3 SRR1118628 GCA_001728155.1 MITN00000000 SAMN02368298 59 5,122,961 146 4,694
CFSAN011189 CDC_K5010-2 Clinical MA 16-Sep-06 O1:Kuk ST3 SRR1118627 GCA_001728175.1 MITO00000000 SAMN02368299 94 5,118,895 134 4,676
CFSAN011190 CDC_K5058 Clinical TX 15-May-07 O3:K6 ST3 SRR1057385 GCA_001728205.1 MITP00000000 SAMN02368300 60 5,114,568 84 4,620
CFSAN011191 CDC_K5059-1 Clinical TX 10-May-07 O5:Kuk ST1147 SRR1840761 GCA_001728215.1 MITQ00000000 SAMN02368301 83 4,959,751 80 4,543
CFSAN011192 CDC_K5059-2 Clinical TX 10-May-07 O5:Kuk ST1147 SRR1057386 GCA_001728235.1 MITR00000000 SAMN02368302 458 4,953,954 123 4,513
CFSAN011193 CDC_K5067 Clinical SD 28-Apr-07 O1:K56 ST775 SRR1118626 GCA_001728255.1 MITS00000000 SAMN02368303 70 5,028,824 144 4,585
CFSAN011194 CDC_K5073 Clinical MD 10-Mar-07 O3:K56 ST750 SRR1118625 GCA_001728275.1 MITT00000000 SAMN02368304 85 5,082,152 117 4,604
CFSAN011195 CDC_K5125 Clinical MS 11-Jun-07 O3:Kuk ST772 SRR1840760 GCA_001728295.1 MITU00000000 SAMN02368305 71 5,134,866 134 4,699
CFSAN011196 CDC_K5126 Clinical MS 21-May-07 O3:Kuk ST1131 SRR1118624 GCA_001728325.1 MITV00000000 SAMN02368306 112 5,143,760 152 4,680
CFSAN011197 CDC_K5276 Clinical NY 20-Apr-07 O11:Kuk ST631 SRR1118623 GCA_001728335.1 MITW00000000 SAMN02368307 86 5,168,475 136 4,774
CFSAN011198 CDC_K5277 Clinical WA No date reported O1:Kuk ST65 SRR1118622 GCA_001728345.1 MITX00000000 SAMN02368308 71 5,165,700 109 4,699
CFSAN011199 CDC_K5278 Clinical WA 25-Jun-07 O4:K12 ST36 SRR1118621 GCA_001728405.1 MITY00000000 SAMN02368309 69 5,067,491 112 4,639
CFSAN011200 CDC_K5279 Clinical WA No date reported O1:Kuk ST65 SRR1118620 GCA_001727655.1 MITZ00000000 SAMN02368310 76 5,172,883 93 4,683
CFSAN011201 CDC_K5280 Clinical WA 11-Jul-07 O4:K12 ST36 SRR1118619 GCA_001727625.1 MIUA00000000 SAMN02368311 87 5,083,288 110 4,662
CFSAN011202 CDC_K5281 Clinical WA 13-Jul-07 O4:K12 ST36 SRR1118618 GCA_001727575.1 MIUB00000000 SAMN02368312 102 5,077,351 115 4,661
CFSAN011203 CDC_K5282 Clinical HI 24-May-07 O5:Kuk Untyped SRR1118617 GCA_001727545.1 MIUC00000000 SAMN02368313 50 4,963,427 79 4,506
CFSAN011204 CDC_K5306 Clinical GA 23-Jul-07 O4:K9 ST34 SRR1118616 GCA_001727505.1 MIUD00000000 SAMN02368314 122 5,045,861 117 4,636
CFSAN011205 CDC_K5308 Clinical AK 14-May-07 O4:K63 ST36 SRR1118615 GCA_001727485.1 MIUE00000000 SAMN02368315 138 5,095,192 142 4,669
CFSAN011206 CDC_K5323-1 Clinical VA No date reported O5:K17 ST674 SRR1118614 GCA_001727475.1 MIUF00000000 SAMN02368316 30 5,220,531 98 4,795
CFSAN011207 CDC_K5323-2 Clinical VA No date reported O5:Kuk ST674 SRR1118613 GCA_001727415.1 MIUG00000000 SAMN02368317 57 5,207,450 84 4,779
CFSAN011208 CDC_K5324-1 Clinical VA 17-Jun-07 O1:K20 ST1132 SRR1118612 GCA_001727395.1 MIUH00000000 SAMN02368318 56 5,272,305 294 4,941
CFSAN011209 CDC_K5324-2 Clinical VA 17-Jun-07 O1:K20 ST1132 SRR1118611 GCA_001727405.1 MIUI00000000 SAMN02368319 70 5,060,178 174 4,654
CFSAN011210 CDC_K5328 Clinical IN No date reported O4:K12 ST36 SRR1118610 GCA_001727665.1 MIUJ00000000 SAMN02368320 85 5,090,169 118 4,661
CFSAN011211 CDC_K5330 Clinical TX 23-Apr-07 O5:Kuk ST1713 SRR1815543 GCA_001728375.1 MIUK00000000 SAMN03358832 43 5,168,109 142 4,757
CFSAN011212 CDC_K5331 Clinical GA 8-Aug-07 O4:K8 ST265 SRR1118609 GCA_001728425.1 MIUL00000000 SAMN02368321 90 5,067,258 95 4,631
CFSAN011213 CDC_K5345-1 Clinical IA 7-Aug-07 O4:K12 ST36 SRR1840759 GCA_001728465.1 MIUM00000000 SAMN02368322 49 5,148,490 112 4,719
CFSAN011214 CDC_K5345-2 Clinical IA 7-Aug-07 O4:K12 ST36 SRR1118608 GCA_001728485.1 MIUN00000000 SAMN02368323 70 5,125,535 105 4,687
CFSAN011215 CDC_K5346 Clinical PA 21-Aug-07 O4:K12 ST36 SRR1815544 GCA_001728415.1 MIUO00000000 SAMN03358833 53 5,087,978 126 4,661
CFSAN011216 CDC_K5428 Clinical NV 6-Jul-07 O1:Kuk ST199 SRR1815545 GCA_001727645.1 MIUP00000000 SAMN03358834 20 5,092,714 139 4,639
CFSAN011217 CDC_K5429 Clinical NV 9-Aug-07 O4:K12 ST36 SRR1118607 GCA_001727585.1 MIUQ00000000 SAMN02368324 111 5,079,150 118 4,653
CFSAN011218 CDC_K5433 Clinical WA 24-Jul-07 O4:Kuk ST36 SRR1118606 GCA_001727385.1 MIUR00000000 SAMN02368325 47 5,073,057 118 4,635
CFSAN011219 CDC_K5435 Clinical WA 11-Aug-07 O1:Kuk ST65 SRR1118605 GCA_001727465.1 MIUS00000000 SAMN02368326 73 5,168,466 105 4,718
CFSAN011221 CDC_K5437 Clinical WA 2-Sep-07 O4:Kuk ST36 SRR1118604 GCA_001727565.1 MIUT00000000 SAMN02368328 122 5,068,632 126 4,636
CFSAN011222 CDC_K5438 Clinical WA 9-Sep-07 O1:Kuk ST65 SRR1118603 GCA_001727705.1 MIUU00000000 SAMN02368329 114 5,154,409 123 4,705
CFSAN011223 CDC_K5439 Clinical WA 19-Sep-07 O4:K8 ST189 SRR1118602 GCA_001728495.1 MIUV00000000 SAMN02368330 36 5,031,538 106 4,616
CFSAN011224 CDC_K5456 Clinical WA No date reported O4:Kuk ST36 SRR1118601 GCA_001728545.1 MIUW00000000 SAMN02368331 119 5,080,809 114 4,656
CFSAN011225 CDC_K5457 Clinical WA 7-Aug-07 O4:Kuk ST36 SRR1118600 GCA_001728505.1 MIUX00000000 SAMN02368332 72 5,075,290 98 4,625
CFSAN011226 CDC_K5485 Clinical NC 8-Jul-07 O6:K18 ST50 SRR1815546 GCA_001728565.1 MIUY00000000 SAMN03358835 23 5,151,277 128 4,719
CFSAN011227 CDC_K5512 Clinical OK 14-Jun-07 O4:K12 ST36 SRR1118599 GCA_001728585.1 MIUZ00000000 SAMN02368333 92 5,082,991 122 4,670
CFSAN011228 CDC_K5528 Clinical GA 6-Oct-07 O4:K68 ST3 SRR1118598 GCA_001728625.1 MIVA00000000 SAMN02368334 128 5,101,848 108 4,645
CFSAN011229 CDC_K5579 Clinical IN No date reported O4:K63 ST43 SRR1118597 GCA_001728575.1 MIVB00000000 SAMN02368335 107 5,072,634 169 4,659
CFSAN011230 CDC_K5582 Clinical GA 10-Oct-07 O11:Kuk ST631 SRR1840757 GCA_001728645.1 MIVC00000000 SAMN02368336 54 5,174,332 104 4,798
CFSAN011232 CDC_K5618 Clinical NY 16-Aug-07 O10:Kuk ST636 SRR1815547 GCA_001728655.1 MIVD00000000 SAMN03358836 25 5,107,768 147 4,668
CFSAN011233 CDC_K5620 Clinical NY 23-Aug-07 O10:Kuk ST636 SRR1815548 GCA_001728665.1 MIVE00000000 SAMN03358837 24 5,099,609 131 4,658
CFSAN011235 CDC_K5629 Clinical GA 18-Nov-07 O4:K13 ST36 SRR1118596 GCA_001728695.1 MIVF00000000 SAMN02368339 106 5,077,945 119 4,659
CFSAN011236 CDC_K5635 Clinical MD 3-Sep-07 O5:K30 ST753 SRR1118595 GCA_001728725.1 MIVG00000000 SAMN02368340 118 5,070,117 110 4,636
CFSAN011237 CDC_K5638 Clinical MD No date reported O4:K12 ST36 SRR1815549 GCA_001728745.1 MIVH00000000 SAMN03358838 29 5,144,324 125 4,733
CFSAN011238 CDC_K5701 Clinical OR 9-Sep-07 O1:Kuk ST65 SRR1815550 GCA_001728755.1 MIVI00000000 SAMN03358839 24 5,157,591 93 4,707
CFSAN011096 GCSL_R5 Oyster TX 14-Mar-07 O10:Kuk ST1133 SRR1815527 GCA_001726165.1 MIQI00000000 SAMN03358816 43 5,185,921 95 4,792
CFSAN011097 GCSL_R6 Oyster TX 14-Mar-07 O10:Kuk ST1133 SRR1118691 GCA_001726265.1 MIQJ00000000 SAMN02368223 80 5,087,599 107 4,659
CFSAN011098 GCSL_R7 Oyster TX 14-Mar-07 O10:Kuk ST1134 SRR1840772 GCA_001726195.1 MIQK00000000 SAMN02368224 44 5,052,796 138 4,640
CFSAN011099 GCSL_R8 Oyster TX 14-Mar-07 O10:Kuk ST1134 SRR1118690 GCA_001726185.1 MIQL00000000 SAMN02368225 52 5,200,929 119 4,807
CFSAN011100 GCSL_R10 Oyster FL 19-Mar-07 O1:Kuk ST313 SRR1815528 GCA_001726175.1 MIQM00000000 SAMN03358817 38 5,072,012 158 4,665
CFSAN011101 GCSL_R12 Oyster LA 27-Mar-07 O4:K8 ST32 SRR1118689 GCA_001726245.1 MIQN00000000 SAMN02368226 43 5,051,895 116 4,618
CFSAN011102 GCSL_R13 Oyster LA 27-Mar-07 O4:K10 ST732 SRR1118688 GCA_001726255.1 MIQO00000000 SAMN02368227 135 5,122,134 161 4,694
CFSAN011103 GCSL_R16 Oyster FL 30-Apr-07 O4:K9 ST34 SRR1118687 GCA_001726345.1 MIQP00000000 SAMN02368228 56 5,051,817 114 4,638
CFSAN011104 GCSL_R17 Oyster FL 30-Apr-07 O4:Kuk ST536 SRR1118686 GCA_001726275.1 MIQQ00000000 SAMN02368229 60 5,010,966 100 4,576
CFSAN011105 GCSL_R21 Oyster TX 4-May-07 O5:Kuk ST12 SRR1118685 GCA_001726355.1 MIQR00000000 SAMN02368230 56 5,124,206 80 4,685
CFSAN011106 GCSL_R26 Oyster NJ 16-Jun-07 O4:K8 ST32 SRR1118684 GCA_001726335.1 MIQS00000000 SAMN02368231 119 5,067,308 134 4,629
CFSAN011107 GCSL_R29 Oyster FL 27-May-07 O11:Kuk ST734 SRR1118683 GCA_001726325.1 MIQT00000000 SAMN02368232 40 4,946,371 104 4,525
CFSAN011108 GCSL_R30 Oyster FL 27-May-07 O1:Kuk ST23 SRR1118682 GCA_001726405.1 MIQU00000000 SAMN02368233 45 5,132,857 131 4,724
CFSAN011109 GCSL_R31 Oyster LA 27-May-07 O1:Kuk ST23 SRR1118681 GCA_001726415.1 MIQV00000000 SAMN02368234 102 5,126,812 132 4,710
CFSAN011110 GCSL_R32 Oyster LA 30-May-07 O10:Kuk ST1142 SRR1057384 GCA_001726445.1 MIQW00000000 SAMN02368235 27 5,059,594 105 4,607
CFSAN011111 GCSL_R33 Oyster LA 30-May-07 O3:Kuk ST28 SRR1118680 GCA_001726435.1 MIQX00000000 SAMN02368236 109 5,043,795 94 4,586
CFSAN011112 GCSL_R42 Oyster WA 26-Jul-07 O10:Kuk ST1155 SRR1815529 GCA_001726485.1 MIQY00000000 SAMN03358818 39 4,981,892 105 4,522
CFSAN011113 GCSL_R45 Oyster WA Jun/Jul-07 (exact date unknown) O5:Kuk ST61 SRR1118679 GCA_001726495.1 MIQZ00000000 SAMN02368237 130 4,973,945 101 4,533
CFSAN011114 GCSL_R47 Oyster AL 20-Apr-07 O4:K8 ST32 SRR1118678 GCA_001726515.1 MIRA00000000 SAMN02368238 66 5,064,518 151 4,653
CFSAN011115 GCSL_R51 Oyster AL 6-Jul-07 O8:Kuk ST676 SRR1815530 GCA_001726535.1 MIRB00000000 SAMN03358819 34 5,049,334 105 4,632
CFSAN011116 GCSL_R52 Oyster WA 13-Jul-07 O3:Kuk ST735 SRR1118677 GCA_001726565.1 MIRC00000000 SAMN02368239 84 5,189,100 86 4,693
CFSAN011117 GCSL_R53 Oyster WA 13-Jul-07 O3:Kuk ST735 SRR1118676 GCA_001726575.1 MIRD00000000 SAMN02368240 68 5,191,609 101 4,715
CFSAN011118 GCSL_R54 Oyster WA 13-Jul-07 O3:Kuk ST735 SRR1118675 GCA_001726595.1 MIRE00000000 SAMN02368241 48 5,189,492 96 4,715
CFSAN011119 GCSL_R55 Oyster WA 14-Jul-07 O3:Kuk ST735 SRR1118674 GCA_001726615.1 MIRF00000000 SAMN02368242 62 5,191,687 100 4,718
CFSAN011120 GCSL_R56 Oyster WA 14-Jul-07 O3:Kuk ST735 SRR1118673 GCA_001726655.1 MIRG00000000 SAMN02368243 111 5,191,187 101 4,715
CFSAN011121 GCSL_R57 Oyster WA 14-Jul-07 O3:Kuk ST1148 SRR1118672 GCA_001726645.1 MIRH00000000 SAMN02368244 72 5,100,768 101 4,652
CFSAN011122 GCSL_R59 Oyster ME 23-Jul-07 O5:Kuk ST113 SRR1815531 GCA_001726685.1 MIRI00000000 SAMN03358820 38 4,953,641 128 4,512
CFSAN011123 GCSL_R60 Oyster ME 23-Jul-07 O10:Kuk ST1135 SRR1118671 GCA_001726695.1 MIRJ00000000 SAMN02368245 77 5,264,840 126 4,802
CFSAN011125 GCSL_R62 Oyster ME 23-Jul-07 O1:Kuk ST1136 SRR1840771 GCA_001726725.1 MIRK00000000 SAMN02368247 34 5,176,254 126 4,777
CFSAN011126 GCSL_R63 Oyster ME 23-Jul-07 O4:Kuk Untyped SRR1118669 GCA_001726735.1 MIRL00000000 SAMN02368248 44 5,125,149 102 4,684
CFSAN011128 GCSL_R65 Oyster ME 23-Jul-07 O5:Kuk ST1150 SRR1118668 GCA_001726755.1 MIRM00000000 SAMN02368250 46 5,065,957 111 4,652
CFSAN011129 GCSL_R74 Oyster VA 23-Aug-07 O4:K34 ST108 SRR1118667 GCA_001726775.1 MIRN00000000 SAMN02368251 90 5,041,370 100 4,605
CFSAN011130 GCSL_R75 Oyster VA 23-Aug-07 O8:Kuk ST676 SRR1118666 GCA_001726805.1 MIRO00000000 SAMN02368252 84 5,014,250 87 4,578
CFSAN011131 GCSL_R76 Oyster VA 23-Aug-07 O8:Kuk ST676 SRR1118665 GCA_001726815.1 MIRP00000000 SAMN02368253 108 5,017,829 82 4,588
CFSAN011132 GCSL_R77 Oyster VA 23-Aug-07 O8:Kuk ST676 SRR1118664 GCA_001726845.1 MIRQ00000000 SAMN02368254 74 5,014,463 76 4,581
CFSAN011133 GCSL_R86 Oyster FL 13-Aug-07 O6:Kuk ST737 SRR1118663 GCA_001726885.1 MIRR00000000 SAMN02368255 70 4,894,547 116 4,454
CFSAN011134 GCSL_R87 Oyster FL 13-Aug-07 O8:K70 ST320 SRR1118662 GCA_001726855.1 MIRS00000000 SAMN02368256 57 5,180,717 111 4,794
CFSAN011135 GCSL_R88 Oyster FL 13-Aug-07 O8:K70 ST320 SRR1840769 GCA_001726895.1 MIRT00000000 SAMN02368257 34 5,184,181 90 4,781
CFSAN011137 GCSL_R95 Oyster PEI 31-Jul-07 O3:K5 ST1151 SRR1118660 GCA_001726915.1 MIRU00000000 SAMN02368259 44 5,123,173 103 4,657
CFSAN011138 GCSL_R96 Oyster PEI 31-Jul-07 O11:Kuk ST1152 SRR1815532 GCA_001726935.1 MIRV00000000 SAMN03358821 41 4,931,576 111 4,466
CFSAN011140 GCSL_R98 Oyster PEI 31-Jul-07 O3:K5 ST1151 SRR1815533 GCA_001726965.1 MIRW00000000 SAMN03358822 41 5,125,449 102 4,676
CFSAN011141 GCSL_R99 Oyster PEI 31-Jul-07 O3:K5 ST1151 SRR1118658 GCA_001726985.1 MIRX00000000 SAMN02368261 74 5,120,561 96 4,647
CFSAN011143 GCSL_R108 Oyster PEI 31-Jul-07 O3:K5 ST1151 SRR1815534 GCA_001726975.1 MIRY00000000 SAMN03358823 68 5,156,159 93 4,700
CFSAN011144 GCSL_R109 Oyster PEI 31-Jul-07 O3:K5 ST1151 SRR1840768 GCA_001727025.1 MIRZ00000000 SAMN02368263 34 5,124,297 93 4,665
CFSAN011145 GCSL_R110 Oyster PEI 31-Jul-07 O3:K5 ST1151 SRR1815535 GCA_001727055.1 MISA00000000 SAMN03358824 54 5,123,801 96 4,673
CFSAN011146 GCSL_R111 Oyster PEI 31-Jul-07 O11:Kuk ST1152 SRR1118656 GCA_001727065.1 MISB00000000 SAMN02368264 102 4,925,276 95 4,459
CFSAN011147 GCSL_R125 Oyster FL 14-Oct-07 O11:Kuk ST739 SRR1118655 GCA_001727045.1 MISC00000000 SAMN02368265 138 5,078,459 150 4,628
CFSAN011148 GCSL_R126 Oyster FL 14-Oct-07 O4:K42 ST1146 SRR1118654 GCA_001727105.1 MISD00000000 SAMN02368266 103 5,138,446 115 4,714
CFSAN011149 GCSL_R129 Oyster FL 1-Oct-07 O11:Kuk ST1153 SRR1118653 GCA_001727115.1 MISE00000000 SAMN02368267 71 4,897,642 115 4,452
CFSAN011150 GCSL_R130 Oyster FL 1-Oct-07 O4:K37 ST1140 SRR1118652 GCA_001975475.1 MUCG00000000 SAMN02368268 76 5,057,199 108 4,594
CFSAN011151 GCSL_R131 Oyster FL 1-Oct-07 O10:Kuk ST1141 SRR1840767 GCA_001727125.1 MISF00000000 SAMN02368269 41 5,140,538 132 4,688
CFSAN011152 GCSL_R135 Oyster SC 21-Nov-07 O3:Kuk ST741 SRR1840766 GCA_001727155.1 MISG00000000 SAMN02368270 34 5,066,023 141 4,634
CFSAN011153 GCSL_R136 Oyster SC 21-Nov-07 O1:K20 ST775 SRR1118651 GCA_001727185.1 MISH00000000 SAMN02368271 78 5,098,402 139 4,651
CFSAN011154 GCSL_R137 Oyster SC 21-Nov-07 O1:K20 ST775 SRR1118650 GCA_001727195.1 MISI00000000 SAMN02368272 71 5,107,378 127 4,638
CFSAN011155 GCSL_R138 Oyster SC 21-Nov-07 O1:K20 ST775 SRR1118649 GCA_001727205.1 MISJ00000000 SAMN02368273 65 5,108,840 148 4,657
CFSAN011156 GCSL_R143 Oyster FL Nov-07 (exact date unknown) O5:Kuk ST743 SRR1118648 GCA_001727265.1 MISK00000000 SAMN02368274 280 4,947,784 148 4,510
CFSAN011157 GCSL_R144 Oyster FL Nov-07 (exact date unknown) O5:Kuk ST1149 SRR1815536 GCA_001727275.1 MISL00000000 SAMN03358825 48 5,085,869 181 4,641
CFSAN011158 GCSL_R145 Oyster FL Nov-07 (exact date unknown) O5:Kuk ST1149 SRR1118647 GCA_001727295.1 MISM00000000 SAMN02368275 64 5,074,938 156 4,624
CFSAN011159 GCSL_R146 Oyster FL Nov-07 (exact date unknown) O5:Kuk ST1149 SRR1815537 GCA_001727245.1 MISN00000000 SAMN03358826 50 5,084,933 172 4,649
CFSAN011160 GCSL_R149 Oyster FL 19-Mar-07 O1:Kuk ST313 SRR1118646 GCA_001727305.1 MISO00000000 SAMN02368276 61 5,067,880 152 4,641
CFSAN011161 GCSL_R150 Oyster FL 19-Mar-07 O1:Kuk ST313 SRR1118645 GCA_001727345.1 MISP00000000 SAMN02368277 25 5,066,369 149 4,663
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a

The state for oyster isolates is the state where the product was harvested; for clinical isolates, this is the state that reported the isolate/illness. PEI, Prince Edward Island.

b

The date for oyster isolates is the date of harvest of the product; for clinical isolates, this is the date of isolation from the patient.

However, some geographic structure among the STs of oyster isolates is apparent. Most STs were isolated from only one site (Fig. 2A). Exceptions to this are ST676, which was isolated from Alabama and Virginia; ST23, which was isolated from Florida and Louisiana; ST32, which was isolated from Alabama, Louisiana, and New Jersey; and ST1152, which was isolated from Maine and Prince Edward Island (Canada). With the exception of Maine, all sites lacking a dominant ST were on the Gulf Coast.

FIG 2.

FIG 2

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Geographic distribution of V. parahaemolyticus isolates from oysters. (A) Geographic distribution of the identified STs. The map was constructed using Python 2.7 with the help of Basemap from mpl_toolkits.basemap and inset_axes from mpl_toolkits.axes_grid1.inset_locator. Pie charts were added at approximate locations of collection sites as demonstrated at http://www.geophysique.be/2010/11/15/matplotlib-basemap-tutorial-05-adding-some-pie-charts/. The size of each pie chart is proportional to the total number of isolates from that location. Pie slices are proportional to the number of times that an ST was isolated at each site. (B) Genetic/geographic structure among the oyster isolates. STRUCTURE was used with MLST genes and geographic locations as priors. The maximum value of ln P(D) corresponded to a k value of 5, which was the lowest k value for which the members within each cluster with an Fst value of >0.2 shared genes. Graphs were created in R version 3.2.2 using the default plotting functions.

To further test the hypothesis that there was geographically meaningful population structure among the oyster isolates, STRUCTURE was used and identified three supported clusters (Fig. 2B). One of these (cluster S1) included ST32 and ST34 isolates primarily from the Gulf Coast (Alabama, Louisiana, and Florida) but also had a representative isolate from New Jersey. Another cluster (S2) consisted of only ST676 isolates from both Alabama and Virginia. The third cluster (S3) included ST23, ST28, ST775, ST1153, and ST1148 isolates, which had representatives from several locations in the Southeast (Florida, Louisiana, and South Carolina) plus one from Washington.

Phylogenetics and clustering.

When the early-diverging bipartitions of the likelihood tree are considered, 9 partitions are identified (Fig. 3). Of these, four (partitions 3, 4, 5, and 7) are composed of only clinical isolates, three (partitions 1, 2, and 6) are composed of only oyster isolates, and two (partitions 8 and 9) contain both clinical and oyster isolates. Seven of the identified bipartitions (1 to 7) consist of only one ST. STs that include multiple serotypes and are monophyletic in the likelihood tree are ST3, ST36, and ST636. Whole-genome distance analysis was also conducted and largely supported the likelihood analysis, as eight of the well-supported partitions in the likelihood tree were also found in the distance tree (Fig. S2 and Table S1), lending more support to these clades.

FIG 3.

FIG 3

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Likelihood tree demonstrating the phylogenetic relatedness of the 132 V. parahaemolyticus isolates. The phylogenetic relationships between isolates from clinical and oyster sources were based on a core genome of 3,726 genes. The likelihood tree was created with RAxML with the GTRCAT model with 100 bootstrap replicates based on the core genome. The 12 largest supported (bootstrap values of >70) clades are colored. Clinical isolates are labeled in red, and oyster isolates are labeled in blue. Clusters identified by STRUCTURE are identified by “SX,” where “X” is the cluster number provided in the STRUCTURE analysis.

Two of the three clusters found by STRUCTURE analysis of the oyster isolates (Fig. 2B) were monophyletic based on the phylogenetic analysis. ST32 and ST34 isolates were clustered by STRUCTURE (Fig. 2, S1) and form a well-supported monophyletic clade in the likelihood tree (Fig. 3, partition 9). ST676 isolates (Fig. 2, S2) from two locations, Virginia and Alabama, form a monophyletic group (Fig. 3, partition 6). From the third STRUCTURE cluster (Fig. 2, S3), ST23, ST775, and ST1153 isolates are all part of a large partition (Fig. 3, partition 8) but do not form a monophyletic group. ST28 and ST1148 isolates, which were also part of cluster S3 by STRUCTURE, were not part of a well-supported clade with those STs in either the likelihood or whole-genome distance analysis trees (Fig. 3; Table S1).

Comparison of the genomes using k-mer-based pairwise distance emphasized the higher diversity among the V. parahaemolyticus isolates (Fig. 4A) than what is commonly seen in other bacterial species. This analysis also enabled the differentiation of related groups that were distributed spatiotemporally, suggesting that they are phylogenetically linked. Further analysis with population partitioning using nucleotide k-mers (PopPUNK) identified five primary clusters, each containing isolates of a single ST (Fig. 4B): ST36 (17 isolates), ST1151 (6 isolates), ST3 (6 isolates), ST735 (5 isolates), and ST65 (5 isolates). Some smaller genomic clusters of four or fewer isolates also containing a single ST (ST676 and -775) were identified. Interestingly, PopPUNK clusters 1, 3, and 5 were composed of the same STs (ST36, -3, and -65) as the monophyletic clades of clinical isolates by the likelihood analysis (Fig. 3, partitions 4, 3, and 5, respectively) but were spatiotemporally separate in this analysis. PopPUNK clusters 2 and 4 and one of the smaller clusters (<5 isolates) were composed of ST1151, -735, and -676 found in the monophyletic clades of oyster isolates in the likelihood analysis (Fig. 3, partitions 2, 4, and 6; Table S1).

FIG 4.

FIG 4

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Vibrio parahaemolyticus genome relatedness using whole-genome distance comparisons. (A) Genome distance using k-mers with an all-against-all comparison. (B) Genomically related isolate network using PopPUNK to demonstrate specific genomic clusters that are epidemiologically related within a cluster but genetically distinct between the groups. Colors represent the same genomic group. The distance between groups indicates relatedness as determined using whole-genome distance. Clusters are labeled with the unique cluster identification from this analysis (clusters 1 to 5), the ST that comprises the cluster, and the likelihood tree partition to which the isolates belong (“LX,” where “X” is the partition number in Fig. 3).

Potential pathogenicity genes.

To visualize a potential pattern between T3SS and TDH family genes and pathogenicity, a heat map was constructed with the isolates sorted by the total number of T3SS and TDH family genes that each possesses (Fig. 5). In most cases, when PCR detected both tdh and trh, InterPro annotated the two genes as tdh. However, there were two instances (ST636_CDC_K4558-2 and ST772_CDC_K5125) where PCR did not detect either tdh or trh but two tdh-related genes were found in the genomic sequence. In addition, there were some instances where PCR detected trh and multiple instances of tdh-related genes were found in the genomic sequence: two in some isolates (ST775_FDA_R137, ST1134_FDA_R7, and ST1134_FDA_R8) and, surprisingly, even three for some ST3 isolates (CDC_K5528, CDC_5010-1, CDC_5010-2, and CDC_4637-2). Isolates from both humans and oysters were among those predicted to have two tdh-related genes.

FIG 5.

FIG 5

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Heat map of counts for SEED and InterProScan annotations. Counts of annotations for SEED T3SS genes and InterProScan annotations for TDH genes and PCR results for tdh and trh are shown. Per-isolate counts for T3SS and TDH annotated clusters along withprevious PCR results for TDH and TRH, included for comparison, were visualized within a heat map created with Plotnine, geom_tile. For ease of visualization, isolates are listed in the order of phylogeny based on the likelihood tree (i.e., isolates at the top are in partition 1 of Fig. 3).

Twenty-six T3SS genes were present in all 132 draft genomes (Fig. 5). There were also several genes found in only some genomes: the T3SS inner membrane P protein was found in 102 isolates, and the T3SS ATPase (FliL), the T3SS FHIPEP sorting domain, and the T3SS substrate exporter were found in 91 isolates. There were 11 isolates that had either additional T3SS genes or extra copies of some T3SS genes: the 7 ST3 isolates, which comprise a monophyletic clade; CDC_K5439, CDC_K5331, and FDA_R130, which cluster near the ST3 isolate clade; and the relatively unrelated isolate FDA_R125 (Fig. 3). There were 5 T3SS genes that were possessed by only these 11 isolates: the injected virulence protein YopP/YopJ, a putative T3SS apparatus protein, the putative T3SS protein Spa33, the putative T3SS system EscC protein, and the putative T3SS system lipoprotein precursor EprK. The same 11 isolates had second copies of two genes: the T3SS host injection protein YopB and the inner membrane channel protein LcrD/HrcV/EscV/SsaV.

Due to the variation in copy number within the T3SS and tdh gene groups, annotations that were prevalent in isolates within monophyletic clades of clinical isolates (ST3, ST36, and ST65) (Fig. 3, partitions 3, 4, and 5) relative to monophyletic clades of oyster isolates (ST676, ST735, and ST1151) (Fig. 3, partitions 6, 1, and 2) were examined, omitting isolates from mixed clades (Fig. 6). While some analyses strictly control for phylogeny, we wanted to be able to determine if gene abundances were consistent within the clades and not only across clades (or isolation source). In some cases, genes with an annotation were abundant, with all isolates having more than 15 genes with the annotation: major facilitator superfamily (MFS) transporters, EAL domains, and acyl-CoA N-acyltransferases. In other cases, the overall numbers were low, with all isolates having at least 1 but fewer than 10 genes: thiamine diphosphate-binding, RmlC-like cupin, integrase/recombinase N-terminal, flavin mononucleotide (FMN)-binding split barrel, and 3-keto-5-aminohexanoate cleavage enzymes. With other annotations, some isolates had none, and others had one or two: pyridoxamine 5-phosphate oxidase, FMN dependent; DUF2787; and glycosyltransferase family 11 (GT11). It was also variable as to how differentiated the clinical isolates were from the oyster isolates for all annotations (Fig. 6), the difference in numbers was statistically significant, and it was generally apparent that clinical isolates had more than oyster isolates.

FIG 6.

FIG 6

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Jitter plot with numbers of genes in monophyletic groups. Annotation groups with statistically significant differences between clinical and oyster isolates (P values of <0.01 after Benjamini-Hochberg adjustment [75]) were included. Genes enriched in isolates from monophyletic groups of human isolates relative to monophyletic groups of environmental isolates were found by counting the number of gene clusters that each isolate was in for each annotation. Isolate counts for each annotation for monophyletic human versus environmental isolates were then compared using Kruskal-Wallis tests. The jitter plot was illustrated with Plotnine, geom_jitter. Red indicates clinical isolates, and blue indicates oyster isolates.

DISCUSSION

Population and genetic continuity in the Gulf and East Coasts.

As reported in a previous study using a subset of these isolates, ST36 and ST3 are the most frequent STs in this isolate collection. Both ST36 and ST3 were comprised only of clinical isolates collected on various dates and from multiple regions with O4:K12/O4:Kut and pandemic-related serotypes, respectively, as previously described for these STs (9, 24). However, these data show high diversity regarding the residual STs of the isolates tested regardless of the method of analysis used (likelihood or genome distance).

STRUCTURE identified three clusters with Fst values indicative of population structure along the Gulf Coast and Atlantic Coast of North America and even including one isolate from the Pacific Coast, consistent with the results of others (30, 31). Phylogenetic support was lacking for one of these clusters detected with STRUCTURE. This is not surprising as MLST alleles represent a small portion of each genome and probably should not be used for phylogenetic purposes when larger data sets are available. However, the incongruence between the phylogenetic results and the MLST results could be indicative of horizontal gene transfer between phylogenetically distant strains (32). Indeed, genomic recombination and horizontal gene transfer were inferred using genomic distance analysis by the presence of multiple clusters (i.e., colored dots adjacent to one another and not forming a distinctive monochromatic group). The precise mechanism, as well as the selection pressure, that results in limiting the genotype diversity has not been identified thus far, although seroconversion has been investigated (33), and is a question that requires additional examination.

Oceanic currents, as well as shipping vessels, could play a role in the distribution, diversity, and genetics of V. parahaemolyticus (34, 35). The highest-diversity site was on the Gulf Coast side of Florida, probably influenced by the Yucatan and Loop currents and their small offshoots, which could be carrying organisms picked up by the Caribbean current from Caribbean islands and the northern coast of South America. Alabama seems to have much lower diversity, but there were only two isolates represented by two STs, so the detected diversity was as high as it could have been. The other Gulf Coast sites also have high diversity relative to most non-Gulf sites and could be influenced by the complex offshoots of the Loop and Mexican currents. STs found on both the Gulf and East Coasts could be an indication of connectivity due to the Florida current and the Gulf Stream. The only other site without a clear majority sequence type is Maine, which is near the convergence of the Gulf Stream and the Labrador current, presenting two possible sources of V. parahaemolyticus and a more variable environment that prevents one ST from dominating. The isolate with ST1148 could have been introduced to the Pacific Coast via ballast water, as has been previously suggested for V. parahaemolyticus (35).

Gene content and pathogenicity.

There was good general agreement between previous PCR detections of tdh-related genes and the genomic results. In most cases where there was PCR detection, tdh-related genes were annotated within the genomes, and when both tdh and trh were detected via PCR, multiple copies of tdh-related genes were found in the proteins predicted by InterPro. The few cases where more genes were annotated from the genomic data than were detected via PCR could be due to a single set of primers amplifying in two places or gene sequence diversity. The use of multiple techniques largely validated but also complemented one another. The two approaches together provide multiple lines of evidence for the existence of clinical isolates lacking any tdh or trh gene and showing that while these bacteria may use these genes during the infection process, they are not required or predictive of pathogenicity.

In addition to the hemolysins, T3SS genes are potentially associated with virulence, perhaps by targeting the cytoskeleton, as do many T3SS effectors. Eleven of our isolates, including all ST3 isolates, contained YopP/YopJ, which is involved in inducing apoptosis (36). Thus, these isolates have an additional weapon that they can use to acquire resources from eukaryotic cells. This, however, does not explain why ST36 and ST65 are so frequently isolated from humans, as these two genotypes lacked the YopP/YopJ gene. While the T3SS may be an important tool in the arsenal of V. parahaemolyticus, it does not explain the pattern of pathogenicity observed in this set of isolates.

The monophyletic clusters of clinical isolates (ST3-ST36-ST65) tend to have additional genes with specific annotations compared to isolates within monophyletic groups of environmental isolates. These groups of genes can be broadly grouped functionally into DNA handling, metabolic, and signaling. One of these annotations is for integrase/recombinase, N terminal. The ST3-ST36-ST65 isolates could use additional integrase capacity to acquire genes that could be used in the infection process (37). These isolates are also enriched in genes involved in sensing the environment, signal transduction, and gene expression. Bacteria that tend to thrive in nutrient-enriched environments typically have more genes related to gene expression and signal transduction. EAL domain proteins are involved in the synthesis of cyclic diguanylate used in intracellular signaling, which could be useful in transmitting intracellular signals related to changes in the extracellular milieu (38). Proteins of this type could help these genotypes quickly take advantage of new resources not only in the ocean but also when they find themselves in an unexpected environment, like a human. The ST3-ST36-ST65 isolates also tend to have more MFS transporters. These proteins act as antiporters and symporters that link the transport of ions with the transport of other ions or small organic solutes (39) and thus could help in nutrient absorption. Better nutrient absorption would make more-rapid growth possible.

After detecting a change in their surroundings, the ST3-ST36-ST65 isolates could use genes of metabolic function, in which they tend to be enriched, to support enhanced growth. Some genes involved in carbohydrate metabolism may have direct effects on their pathogenicity. As one example, RmlC-like cupin domains play a role in the synthesis of l-rhamnose that may be involved in bacterial pathogenesis (40). Glycosyltransferase family 11 (GT11) is a galactoside 2-l-fucosyltransferase, which transfers fucose from guanosine-diphosphate fucose to a substrate (41), homologs of which are involved in antigen synthesis in other bacterial species (42, 43). Thus, GT11 could be important in the interaction of V. parahaemolyticus with a human host.

The ST3-ST36-ST65 isolates also appear to be enriched in genes related to the processing of metabolites. Acyl-CoA N-acyltransferases function in the synthesis of fatty acids using acetyl-CoA and another lipid as the substrates and in the production of ATP from amino acids (44, 45). Both functions could be beneficial to V. parahaemolyticus during periods of low-nutrient stress, such as in coastal estuarine habitats. Other marine organisms use acyltransferases and similar proteins to store fatty acids and wax esters for use during periods of stress or dormancy (46, 47). Such a gene could be used by V. parahaemolyticus to rapidly incorporate environmental lipids for more rapid growth or to store them for use in times of stress (48). While there is much to be studied about how these genes enriched in the ST3-ST36-ST65 clade could contribute to environmental survival, transmissibility, and pathogenicity, it is clear that there are many potential benefits.

Rare in the marine environment but common in outbreaks.

It is surprising that ST3, ST36, and ST65, each of which forms a monophyletic clade, were prevalent among clinical isolates but were not recovered from oysters in this study. While seafood-associated outbreaks could be expected to be caused by genetically homogeneous organisms (49), even with the bias in favor of sampling from clinical sources, enough samples were taken from the environment that if ST3, ST36, and ST65 were common in the environment, these STs would be represented by at least a few oyster isolates. However, previous work has found ST3 and ST36 isolates in the environment (15), but that study was focused on an area where these STs were strongly predominant in patients. They may become abundant in marine environments only under specific conditions, remaining in unidentified oceanic reservoirs otherwise. The enhanced lipid storage capability, inferred from the possession of extra copies of acyl-CoA N-acyltransferase genes, of ST3-ST36-ST65 isolates may enable them to spend more time as dormant cells (50, 51) in marine habitats and be numerically inferior to other genotypes of this bacterium, making them more difficult to detect. In contrast, they seem to have some relative advantages in associating with humans. For example, they may be able to take advantage of the more-nutrient-replete conditions of the human intestine more quickly due to their enhanced transport and signaling systems as well as additional metabolic genes that allow them to outcompete other genotypes under these conditions.

Conclusions.

This study used whole-genome sequencing of 132 V. parahaemolyticus isolates to understand the distribution and genetics of this pathogen along coasts of North America. Using in silico MLST from the draft genomes, a hub of genetic diversity along the Gulf and southern Atlantic Coasts as well as lower diversity along the northern portions of the Atlantic and Pacific Coasts were identified. In addition, the data show that the results of MLST methods, which use only a small portion of the genome, are generally consistent with the results of a core genome maximum likelihood phylogeny and that the results of distance phylogenetic methods, when applied to the same genomic data, are broadly consistent with the results of the maximum likelihood analysis. These results indicate that when time, sequencing capacity, or computational capacity is limited, biologists can use faster and more affordable methods and still obtain informative, but not comprehensive, results.

Additionally, this study looked for pathogenicity genes. The tdh, trh, and T3SS genes, while present in many of our isolates, do not explain the apparent differences in pathogenicity. Instead, the isolates from monophyletic clades consisting of only human isolates were enriched in genes having to do with signal transduction, nutrient absorption, energy transduction, and energy storage. This is consistent with a model in which these strains can store energy efficiently for periods of dormancy and then quickly respond to periods of high levels of nutrients by growing quickly and outcompeting other strains of the same bacterial species. Thus, it is possible that the strains most frequently isolated from humans are difficult to detect in marine habitats because they are more likely than other strains to be in a dormant state, but when levels of nutrients become especially high, as in a human intestine, they are able to outcompete their competitors. Further experimental work is needed to elucidate the ability of strains to persist and proliferate under various conditions in relation to the genes possessed and the ecological factors that vary with geography. This experimental work should include gene expression profiling, as pathogenicity could be influenced by gene expression as well as gene content.

MATERIALS AND METHODS

Bacterial strains.

The 61 oyster and 71 clinical well-characterized isolates used in this study (Table 1) were collected from 2006 to 2007 in the United States and Canada (10, 23). These isolates were selected to be representative of the geographic diversity of North America for both environmental (oyster) and patient isolates over a given period to avoid the introduction of broad temporal variation. The isolates were stored at −80°C until analysis.

Genomic DNA extraction.

All isolates were grown in Trypticase soy agar (TSA; Thermo Fisher Scientific, Waltham, MA) overnight at 37°C. High-molecular-weight DNA was extracted using the QIAamp DNA minikit (Qiagen, Valencia, CA). The integrity of high-molecular-weight DNA was determined using a 2200 TapeStation with genomic DNA ScreenTape (Agilent Technologies, Santa Clara, CA) as previously described (52).

Whole-genome sequencing, assembly, and annotation.

Sequencing was conducted by the Weimer laboratory at the University of California—Davis through the 100K Pathogen Genome Project (http://www.genomes4health.org/) (53) as previously described (54). DNA was fragmented using the Covaris (Woburn, MA) E220 ultrasonicator. Fragmented DNA (1 μg) was used to construct sequencing libraries using the HTP library preparation kit (Kapa Biosystems, Wilmington, MA), on a Bravo NGS workstation (Agilent Technologies). Fragmented double-stranded DNA (dsDNA) molecules were end repaired (5′), adenylated (3′), and then ligated with dsDNA adapters using the NEXTflex-96 DNA barcode (Bioo Scientific, Austin, TX). The size distributions of amplified libraries were confirmed using the 2100 bioanalyzer with a high-sensitivity DNA kit (Agilent Technologies). Finally, the indexed libraries were quantified with a quantitative PCR (qPCR)-based library quantification kit (Kapa Biosystems) prior to pooling for sequencing on the Illumina HiSeq 2000 platform with PE100 plus index reads (Illumina, San Diego, CA). The genomic sequences were de novo assembled using SPAdes version 3.1.1 software (55) with a k-mer length of 32. The draft genomes were annotated using the NCBI Prokaryotic Genomes Automatic Annotation Pipeline (PGAAP) (www.ncbi.nlm.nih.gov/genomes/static/Pipeline.html).

Pangenome analysis.

The pangenome was determined as described previously by Bandoy and Weimer (56). Briefly, the genome sequences were assembled using Shovill (https://github.com/tseemann/shovill), annotated using Prokka (57), and used as the data input for pangenome analysis using Roary (58). Assembled genomes were variant called using Snippy (https://github.com/tseemann/snippy). Gene presence/absence was visualized using Phandango (59) with the associated metadata. Gene associations, metadata, and phenotypes were associated using Scoary (60).

Population genetics and biogeography.

Genomes were analyzed for MLST sequence types (STs) based on the allele types of the housekeeping genes dnaE, gyrB, dtdS, recA, pyrC, pntA, and tnaA through the MLST database for V. parahaemolyticus at http://pubmlst.org/vparahaemolyticus/ (21). STRUCTURE, which is a method for inferring population structure without incorporating any a priori information as to membership (61, 62), was run using the MLST alleles from oyster isolates as the input and the geographic collection sites as priors. The burn-in consisted of 30,000 Markov chain Monte Carlo (MCMC) iterations followed by 60,000 additional repetitions that were utilized. Values for k from 2 to 16 were tested. The highest ln P(D) value corresponded to a k value of 5. This k value resulted in all well-supported clusters (Fst > 0.2) sharing alleles, so it was used because there is no genetic basis in this data set for clustering isolates lacking shared alleles.

Phylogenetics.

For the likelihood tree, nucleotide sequences from each cluster of orthologous groups (COG) in the core genome were aligned using MAFFT (version 7.427) with “—adjustdirection” to find reverse complements of genes when necessary to ensure that genes were oriented in the same direction (63). The alignments were then concatenated with a custom Python script. The best likelihood tree as well as the 100 bootstrap trees were found using raxmlHPC-PTHREADS (version 8.2.9) rapid bootstrapping with a general time reversal rate categories (GTRCAT) model of nucleotide substitution and rate heterogeneity (64).

For the distance tree, the assembly of paired-end reads was done with ABySS 1.5.2 using a k value of 64 (65) and as used previously (28, 29). Pairwise distances were determined using the Meier-Kolthoff method as reported previously (66) and implemented locally using PanCake (29). A distance tree was inferred using Mega7 with neighbor joining (67, 68) and visualized using MATLAB software (MathWorks, Natick, MA, USA).

Genome variation was determined using total genomic distance with a k-mer (31-mer) approach as a method to use the entire genome to determine relatedness between isolates as previously described (56). Population partitioning using nucleotide k-mers (PopPUNK) was used to determine related genomic clusters based on the whole-genome distance of the Vibrio isolates used in this study. PopPUNK uses genomic distance with variable-length k-mer comparisons, enabling the analysis of divergence in core and accessory gene contents within the same analysis to visualize a network-like vision of genome diversity (69).

Ortholog discovery and annotation.

Open reading frames were found using Prodigal 2.6.3 (70). The core genome was identified by first running all-against-all pairwise reciprocal BLAST (71), finding pairwise reciprocal best hits (PRBHs) requiring an E value of <1 × 10−45 and a length of at least 80 nucleotides, and then grouping these into COGs (72). COGs were found with a custom Python script that first identified preclusters from a sorted list of all PRBHs by clustering pairs sharing the first element and then recursively looked for and grouped preclusters that overlapped by at least two gene identifications. Clusters overlapping by only one gene identification remained as separate clusters. In these cases, one was removed, with a preference for keeping the larger cluster. The core genome was then assembled from COGs containing every isolate exactly once.

COGs were annotated by BLAST comparisons to isolates AQ3810 and RIMD_2210633 (downloaded from https://theseed.org [73]) and with InterProScan (74) using the RESTful Web service and Python 3.6. The InterPro annotations (identifiers starting with “IPR”) were used. In both cases, annotations with E values of <1 × 10−45 were accepted. For T3SS genes, SEED annotations were preferentially used over InterPro annotations because they were presumed to be more species specific and refined, but if InterPro annotated a gene as being T3SS related, it was used unless the SEED annotation indicated that it was flagellar. TDH family gene clusters were annotated with InterPro.

Data availability.

All accession numbers and associated metadata are provided in Table 1.

Supplementary Material

Supplemental file 1
supp_data_source_01403-20.pdf (1.1MB, pdf)

ACKNOWLEDGMENTS

This project was supported in part by an appointment of C. H. M. Lüdeke and J. J. Miller to the Research Participation Program at the Center for Food Safety and Applied Nutrition administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration.

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Associated Data

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Supplementary Materials

Supplemental file 1
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Data Availability Statement

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