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Comparative genomic analysis of Vibrios yields insights into genes associated with virulence towards C. gigas larvae

Abstract

Background

Vibriosis has been implicated in major losses of larvae at shellfish hatcheries. However, the species of Vibrio responsible for disease in aquaculture settings and their associated virulence genes are often variable or undefined. Knowledge of the specific nature of these factors is essential to developing a better understanding of the environmental and biological conditions that lead to larvae mortality events in hatcheries. We tested the virulence of 51 Vibrio strains towards Pacific Oyster (Crassostreae gigas) larvae and sequenced draft genomes of 42 hatchery-associated vibrios to determine groups of orthologous genes associated with virulence and to determine the phylogenetic relationships among pathogens and non-pathogens of C. gigas larvae.

Results

V. coralliilyticus strains were the most prevalent pathogenic isolates. A phylogenetic logistic regression model identified over 500 protein-coding genes correlated with pathogenicity. Many of these genes had straightforward links to disease mechanisms, including predicted hemolysins, proteases, and multiple Type 3 Secretion System genes, while others appear to have possible indirect roles in pathogenesis and may be more important for general survival in the host environment. Multiple metabolism and nutrient acquisition genes were also identified to correlate with pathogenicity, highlighting specific features that may enable pathogen survival within C. gigas larvae.

Conclusions

These findings have important implications on the range of pathogenic Vibrio spp. found in oyster-rearing environments and the genetic determinants of virulence in these populations.

Background

The Vibrio genus represents a group of microorganisms ubiquitous in temperate marine, freshwater, and estuarine environments [1,2,3]. Members of this genus are known to have roles as commensal or pathogenic members of marine animal microbiomes [4, 5], or as free-living members of the environment [6, 7]. Incidences of diseases attributed to Vibrio bacteria are broadly referred to as “vibriosis”, with the identities of the etiological agents and their pathogenic mechanisms varying between hosts. In aquaculture settings, vibriosis outbreaks can lead to large economic and productivity impacts [8]. Commercial shellfish hatcheries are impacted by disease outbreaks attributed to vibrios, which can result in large mass mortalities of larvae [9,10,11,12].

Genomic sequencing of marine pathogens has aided our understanding of the genetic mechanisms of virulence in some of these systems. Within some species of Vibrio, there are well-defined mechanisms for virulence. For instance, within the Splendidus clade of Vibrios, numerous virulence factors, including an exported protein of unknown function, gene r5.7 [13, 14], species-specific Type 6 Secretion System effectors [15], and the Multifunctional-Autoprocessing Repeats-in-Toxin (MARTX) cluster [14], have been shown to affect Pacific Oyster (Crassostrea gigas) adults and juveniles. The importance of a zinc metalloprotease has also been reported in several Vibrio oyster pathogens, including V. tasmaniensis [16], V. aestuarianus [17], and V. coralliilyticus [18]. V. coralliilyticus, long established to be a coral pathogen [19,20,21], has also been implicated in significant mortality events of larvae at shellfish hatcheries [11, 22,23,24,25] and of C. gigas larvae in laboratory experiments [18, 24,25,26]. The outer membrane protein OmpU and the ToxR transcriptional regulator have also been implicated in virulence of V. coralliilyticus towards C. gigas larvae [27]. However, it is unclear if there are other genetic mechanisms contributing to V. coralliilyticus oyster larvae pathogenesis and what their conservation is across the Vibrio genus and whether other Vibrio strains found within aquaculture settings interact with shellfish and what their impacts on oyster health may be. Genome comparisons of vibrios with varying virulence phenotypes are an imperative step in identifying the genes associated with pathogenesis.

In this study, the genomes of 42 Vibrio isolates were sequenced and compared to identify and characterize conserved genomic features contributing to mortalities of C. gigas larvae. These isolates, along with nine previously sequenced strains, were tested for pathogenicity towards C. gigas larvae. Most isolates exhibiting pathogenic phenotype were identified as belonging to the V. coralliilyticus species group, emphasizing the identity of this species as a notable pathogen. By contrasting phenotypes with genotype data, we present an in-depth comparative analysis into the genomic repertoire of pathogenic and non-pathogenic vibrios found in aquaculture environments. Additionally, we introduce insights into the genomic content of V. coralliilyticus strains, and put forward evidence for what genes may be responsible for V. coralliilyticus vibriosis of C. gigas larvae.

Results

Pathogenicity of Vibrio isolates against C. gigas larvae

Survival of larvae with non-control bacterial strains (n = 49) ranged from 0 to 100%, with an average mortality across all trials of 35.7% (std. dev. 40.3%) and median of 13.0% (Fig. 1). Results approximate a bimodal distribution, with individual isolates displaying either high pathogenicity or having little effect on oyster survival. Members of the high-pathogenicity group consistently caused larvae mortalities between 51 and 100% and primarily included isolates identified as V. coralliilyticus strains. Only two isolates not classified as V. coralliilyticus resulted in high mortalities: strain Vibrio sp. strain RE88 (99% ± 3%) and V. mediterranei strain 71,105 (79% ± 0.10%). The low-pathogenicity group was comprised of a more diverse set of Vibrio species. Thirty-nine isolates tested positive for hemolytic activity on 5% horse blood agar and 44 isolates tested positive for proteolytic activity on 2% skim milk agar (Additional file 1: Table S1).

Fig. 1
figure1

Mortality of C. gigas larvae in pathogenicity experiments tested against 51 Vibrio strains. Boxes represent the median value, the 25th percentile, and the 75th percentile. Outliers represent values greater than 1.5 times outside these percentiles. Percent mortalities within no-bacterial controls averaged 6.8% (std. dev. 0.077)

Genome features, phylogenetics, and comparative genomics

To better understand the genomic potential of hatchery-associated vibrios and identify traits that are associated with virulence, we conducted whole-genome shotgun sequencing, assembly, and annotation of the 42 isolates (Table 1). Isolates spanned a wide range of species within the Vibrio genus. Ten newly sequenced genomes were designated as V. coralliilyticus based on ANI relationships (Additional file 2: Fig. S1). Eight strains did not have > 95% ANI with any reference type strains, preventing species-level classification. One of these, Vibrio sp. RE88, was closely related to the V. coralliilyticus ATCC BAA-450T at 94% ANI, but under the species-threshold of 95–96% ANI. Three strains, T3Y01, 070316B, and 99 K-1, all had over 98% ANI among pairs, but were not related to any other reference genome in the database tested, indicating that these are possibly a novel species group. A universal gene phylogeny supports the taxonomic assignments based on ANI relationships (Fig. 2), and clusters most of the isolates into two main clades: the Coralliilyticus clade and the Splendidus clade. These data were congruent with a phylogenetic tree built from concatenated sequences of the computationally-determined core genome of 154 Vibrionaceae genomes (Additional file 3: Fig. S2), scoring a Kuhner-Felsenstein (KF) [31] distance of 0.209.

Table 1 Bacterial strains and genomes used in this study
Fig. 2
figure2

Phylogeny Genes of Vibrio spp. associated with oyster pathogenicity. Phylogeny of tested isolates and heatmap of hierarchical clustering of presence and absence of genes significantly correlating (p < 0.005) with larval survival in pathogenicity assays. Virulence and phylogeny of Vibrio spp. Phylogenomic tree built from 102 universal conserved genes. Bar plot adjacent to each tip label represents the average percent of mortalities of C. gigas larvae after 72 h of bacterial challenge. Dark blue indicates presence of a gene and grey indicates absence in the heatmap

The core, flex, and pan-genomes of isolate genomes were classified to determine the functional composition of the conserved and unique genes. OrthoMCL [32] clustering defined a total of 1693 core genes, 11,327 flexible genes, and 23,933 genes in the pan genome of sequenced strains. Core and pan-genome gene accumulation plots were created (Additional file 4: Fig. S3), and the curve of the pan-genome plot and the Heaps law model (alpha = 0.47) indicated an open-pan genome amongst these isolates.

The observed distribution of functional categories of the genes significantly correlated with the results of larval pathogenicity assays was significantly different than that of the genes in the core genome (chi-square = 3979.2, df = 19, p < 0.001). The functional categories “Carbohydrate transport and metabolism”, “Transcription”, “Secondary metabolites biosynthesis, transport and catabolism”, “Function unknown”, and “Signal transduction mechanisms” were significantly enriched in the flexible genome relative to the core genome (p < 0.05). The category of genes without any significant hits to the GAMMAPROTNOG database was also significantly enriched in the flex genome (p < 0.05).

Genes associated with C. gigas larvae pathogenicity

To identify genes associated with C. gigas larval mortalities we implemented a phylogenetic logistic regression model using gene presence/absence and pathogenicity assay data for each isolate. A total of 508 genes were identified to have a significant negative correlation with larvae survival (p < 0.005), while only 19 genes had a significant positive correlation with survival (Fig. 2 and Additional file 5: Table S2). Since most of the pathogenic isolates were putatively assigned as V. coralliilyticus species, we used coding sequences from the complete genome sequence of V. coralliilyticus strain RE22 (Accession: GCA_003391375) as a reference for gene annotations, and to compare the synteny of contigs from isolate genome assemblies. Three eggNOG functional categories were significantly enriched in the group of genes correlated with high mortality/low survival phenotypes compared to the functional composition of core genome from all strains (Supporting Fig. 3): “Carbohydrate transport and metabolism” (G; p = 0.0365); “Secondary metabolite biosynthesis, transport and catabolism” (Q; p = 5.27E-06); “Function unknown” (S; p = 4.11E-11); and a fourth category containing genes that did not have significant hits to the database (p = 0). Metabolic genes were a dominant component of these functional categories and included chitinase A precursors, a bile acid symporter, a pectate lyase, and multiple sugar degradation enzymes (Additional file 6: Table S3).

Additional genes found to be negatively correlated with larvae survival included those with annotations putatively involved with host virulence. A group of reb genes were significant (C = 3.48, p = 0.0005) (Table 2), with most being exclusive to and conserved within the V. coralliilyticus group of isolates. The putative reb genes were located within a larger putative genomic island of the RE22 complete genome (~ 30 KB) that encoded multiple genes found to be significant with larvae pathogenicity outcomes, including an endolysin (AXN33720.1), a methyltransferase (AXN33721.1), a putative methyl-accepting chemotaxis protein (AXN33724.1), and multiple hypothetical proteins.

Table 2 Features of the putative Reb-coding region of Vibrio coralliilyticus strain RE22

Genes coding for a Type 1 Secretion System (T1SS) (AXN34790.1, AXN34851.1, and AXN34791.1) were significant based on the phylogenetic logistic regression analysis (p < 0.0005), although this region, located in V. coralliilyticus strain RE22 on the p337 plasmid, was not conserved in all isolates of this species. Additional genetic features with potential virulence functions included a coding sequence with a haem-degrading domain (AXN33843.1; C = 3.48, p = 0.0005), a gene with pore-forming and peptidase domains (AXN34842.1), regulatory proteins and hypothetical proteins (C > 2.8, p < 0.005), a gene coding for a necrosis-inducing protein NPP1 (AXN30583.1; C = 3.48, p = 0.0005), and a gene with a domain similar to the cysteine proteinase peptidase_C58 superfamily (AXN33813.1; C = 5.21, p = 0.0005).

Within the large chromosome of the RE22 genome, a number of syntenic loci whose presences were significantly negatively correlated with larvae survival had annotations for a functional Type 3 Secretion System (T3SS; Fig. 3). The genomic region containing these genes corresponds to the pathogenicity island CPI-1 [33]. In addition to encoding a T3SS and effectors, this region includes genes for multiple transcriptional regulators, a rhombotail lipoprotein, an anthranilate synthase component II, the hemolysin locus vchAB, a glutathione-dependent formaldehyde-activating enzyme (GFA)-related enzyme, and hypothetical proteins (Additional file 7: Fig. S4). This genomic island is conserved in all V. coralliilyticus genomes examined in this study, as well as the closely-related Vibrio sp. strain RE88. It is not encoded in V. mediterranei strain 71,105, though, which was found to cause high mortalities in oyster larvae. The CPI-1 island was not fully present in strains causing low mortality rates. V. pectenicida strain 99–46-Y, which causes low levels of mortality, encodes a partial CPI-1-like island, which encodes the T3SS structural genes and putative effectors (Additional file 8: Fig. S5).

Fig. 3
figure3

Manhattan Plot with P-value results for Vibrio genes correlating with larvae mortalities and ACT plots of the CPI-1 region of six V. coralliilyticus strains. Manhattan plot (top) shows p-values for genes on the Chromosome 1 of V. coralliilyticus RE22 that were tested with a phylogenetic logistic regression for correlation with larvae mortalities. P-values are log-transformed. Blue line corresponds to a significance cut-off of p < 0.005. CPI-1 regions of genomes (bottom) were aligned using BLASTN and results for each pairwise alignment were visualized in Artemis Comparison Tool (ACT) in order to visually compare gene synteny and sequence similarity between CPI-1 regions of V. coralliilyticus strains

A comparison of the CPI-1 region of six V. coralliilyticus genomes revealed that the general architecture of this region ranged from 81 to 112 kb in length and consisted of several regions of conserved gene content interspaced with more variable regions (Fig. 3), including a locus containing an RTX-like coding sequence and T1SS machinery. Phylogenetic analysis of the sctV gene, which encodes a conserved protein of the T3SS, indicated that this gene is more similar to those of species of Yersinia, Salmonella, Escherichia, Edwardsiella and Shewanella, and separate from the sctV genes of V. parahaemolyticus, V. cholerae, V. alginolyticus, and V. harveyi (Additional file 9: Fig. S6).

Discussion

Here, we examined the ability of Vibrio isolates to cause mortality of C. gigas larvae under controlled experiments. Many of these isolates were collected from hatcheries where C. gigas larvae are produced. Comparative analysis of the genomes of these 51 isolates identified genes specifically associated with a pathogenic lifestyle from the large pan-genome. We found a strong phylogenetic signal among the strains that caused high mortalities; 15 out of 17 strains tested resulting in 50% or higher mortality rates were putatively identified as V. coralliilyticus strains, and one, Vibrio sp. RE88, was found to be the closest outgroup to this species. Strain V. mediterranei 71,105, was the lone pathogen not found within this clade. V. coralliilyticus has been previously implicated in disease outbreaks at a West Coast hatchery [11]. Importantly, we did not have a V. coralliilyticus strain that did not cause mortalities in tests with C. gigas larvae, so although we attempted to control for phylogenetic signal with the phylolm package, we cannot definitively assert that the genes detected with this model are directly related to pathogenesis rather than just being a trait of the species serving some other ecological function. Still, in this discussion we highlight gene features, many of which are conserved in the V. coralliilyticus strains we tested, significantly correlated with mortalities, signifying that these genes may set apart pathogens from other vibrios ubiquitous in hatchery and marine environments.

A number of gene clusters negatively correlated with oyster survival were homologous to genes of the CPI-1 pathogenicity island of V. coralliilyticus ATCC BAA-450 [33]. Pathogenicity islands are a common way for bacteria to distribute large sets of virulence genes via horizontal gene transfer [34]. The most conspicuous virulence factor encoded by the CPI-1 is a putative T3SS. T3SSs are often used by pathogenic bacteria to invade or manipulate host cells. For example, the T3SS-2 of Vibrio parahaemolyticus facilitates host cell invasion [35]. The observation that the conserved T3SS gene, sctV, is more similar to non-Vibrio T3SS genes is consistent with a previous phylogenetic analysis [36] and suggests that the T3SS, and perhaps the surrounding the CPI-1 island, was acquired through horizontal gene transfer.

It is important to note that, although V. pectenicida 99–46-Y appears to encode a relatively complete CPI-1, it was not virulent in our testing. This allows for a comparison of the gene content of the CPI-1 from pathogens with that of the CPI-1 of V. pectenicida 99–46-Y in order to identify those genes that may be specific to oyster larvae pathogenesis. Genes located in CPI-1 of C. gigas larvae pathogens, but absent in the non-pathogenic V. pectenicida 99–46-Y, included those annotated as the vchA hemolysin (AXN30429.1) and another secreted lysin, vchB (AXN30428.1). These genes are highly similar to two homologs of V. vulnificus (vvhA [100% coverage and 76.86% identity], vvhB [100% coverage and 59.52% identity]), which are known to cause epithelial damage and contribute to intestinal growth of the bacterium [37]. Additionally, VchA of V. coralliilyticus, was shown to lyse eukaryotic cells, including erythrocytes [38]. Interestingly, though, culture supernatants from strains of recombinant V. cholerae expressing the vchA gene did not cause significant mortality to C. gigas larvae [18], indicating that although there appears to be a role for this protein in lysing eukaryotic cells, its role as an independent virulence factor in oysters is unclear.

As filter feeding organisms, oysters inherently accumulate large amounts of bacteria from the surrounding water, leading to many coincidental interactions with pathogens. Like other invertebrates, oysters rely on innate immunity and phagocytic cells (e.g., hemocytes in oysters) to protect against pathogenic microbes. Hemocytes are circulatory cells that develop by the early D-veliger larvae stage (17 h post-fertilization) [39] and eliminate bacterial pathogens through specific binding mechanisms, phagocytosis, and subsequent digestion mechanisms [40,41,42,43]. Exposure of C. gigas larvae to pathogenic V. coralliilyticus is known to cause a decline in feeding rate, an activation of the immune response (e.g., hematopoiesis, activation of non-self recognition mechanisms, and production of antimicrobial peptides), and modulation of cell membrane composition [26]. Less is known, though, about the strategies that pathogens employ to escape the immune system of Pacific Oyster. Interestingly, V. coralliilyticus RE22 has been shown to suppress immune signalling pathways of Eastern Oyster (Crassostrea virginica) larvae [44]. Our work expands on these findings by identifying additional conserved mechanisms that Vibrio pathogens may utilize to circumvent immune responses by oyster larvae.

For instance, multiple genes of a ~ 30 Kb-long locus conserved within the genomes of the tested V. coralliilyticus isolates and Vibrio sp. strain RE88 were found to be negatively correlated with larvae survival. This locus includes multiple reb genes and their nearby coding sequences, which are predicted to encode “R-bodies”. R-bodies are insoluble bacterial proteins that confer a phenotype known as the “killing trait” [45]. R-bodies of the intracellular symbiont, Caedibacter, switch between two stable conformations in response to a stimulus such as an extracellular pH change that occurs during phagocytosis. R-bodies of Caedibacter cells, which have been internalized within the lysosomes of a symbiont-free competitor paramecium, will then switch into a needle conformation and rupture the bacterial cell wall, whereupon the cytosolic contents will release unidentified toxins that induce paramecium death [45, 46]. Recombinant Reb proteins from V. nigripulchritudo, a shrimp pathogen, have been visualized by transmission electron micrographs [47], and the reb gene cluster of V. coralliilyticus are proposed to be derived from a horizontal gene transfer event [48], although no known role has been identified in this species. This region of genes appears to be highly conserved in V. coralliilyticus as it is also present in several V. coralliilyticus genomes not included in our study (data not shown). The high correlation of putative reb genes with pathogenicity make this region a target for further investigation to determine its role in virulence or survival within the host, whereby V. coralliilyticus infections may be facilitated by invading and/or modulating the cells of the oyster immune system.

A locus including gene AXN30831.1 was negatively correlated with high rates of larval survival and was annotated to degrade myo-inositol to acetyl-CoA. V. coralliilyticus strain BAA-450 is known to utilize myo-inositol as a carbon source [20]. Pathogens may be encountering inositol in the host environment. For example, host inositol promotes the growth and virulence of Legionella pneumophila within amoeba and host macrophages [49]. Phosphatidylinositols, membrane lipids with a myo-inositol sugar in the headgroup, have been found to be important in C. gigas growth [50]. Furthermore, there is evidence that both phosphatidylinositol and soluble inositol phosphate signalling plays a role in immune response by bivalve hemocytes [42, 51, 52], and that C. gigas larvae challenged with Vibrio pathogens show increased in phosphatidyliniositol content [26]. These findings suggest that pathogenic vibrios may modulate oyster immunity by interfering with inositol phosphate signalling, and/or may be able to use host-derived myo-inositol as a carbon and energy source.

Multiple genes annotated to the COG category for carbohydrate utilization were negatively correlated with high larval survival. Two of these were putatively annotated as chitinase genes. One of these is a chiA homolog (AXN33250.1) that is uniquely encoded within V. coralliilyticus genomes, and is divergent from another chiA homolog found to be conserved among the genomes of all Vibrio isolates tested. A study by Lin et al. [53] noted that the former homolog was likely introduced into the V. coralliilyticus clade via horizontal gene transfer, while the more conserved homolog was hypothesized to be vertically inherited within the genus and under strong purifying selection. Another gene, AXN29974.1, with a partial N- acetylglucosamine-binding protein A domain was also negatively correlated with high larval survival. This domain functions in chitin-binding and in V. cholerae [54].

Genes for chitin binding and degradation are present in almost all Vibrio genomes [53, 55] and chitin metabolism is an important and highly conserved feature, with roles in survival with marine hosts [56], nutrient acquisition [57], and conjugation of extracellular DNA [58]. With regard to oyster hosts, chitin is one of several essential components of their shell matrix [59], and chitin synthase is highly expressed in early stages of C. gigas larval development [60], and in hemocytes and the mantle of adults [60, 61]. Interestingly, growth of V. coralliilyticus strain S2052 on chitin not only induces expression of chitin utilization genes, but also genes related to host colonization, pathogenesis (including reb-type genes), and natural competence [62]. Our observation of a correlation of these putative chitin utilization genes with pathogenicity supports a hypothesis that chitin is a key mediator of interactions between oysters and Vibrio pathogens.

Multiple genes with putative functions for nutrient acquisition were found to be significantly correlated with pathogenicity. Several were proteases, which can aid in colonization of and persistence within a host, degradation of host biomass for energy and carbon acquisition, or the breakdown of accumulated waste [63]. One of these, a S8 serine peptidase (AXN34464.1) was highly similar to a protein (97.54% identity) found in the secretome of a V. coralliilyticus P1 vcpA mutant that is virulent towards Artemia and Symbiodinium [64]. Additional putative metabolic functions found to correlate negatively (Additional file 6: Table S3) with larval survival included a conserved locus spanning three operons with genes coding for disaccharide transport, as well as the high-affinity pstSCAB phosphate transporter. These metabolism features correlated with pathogenicity may reveal strategies and adaptations for colonization of a host or other environments.

Genes vcpA, vcpR, toxR were not found to be significantly associated with pathogenicity, despite being known virulence factors of V. coralliilyticus [18, 27, 65]. These genes were generally found to be conserved among all genomes tested here, precluding detection of statistical relationships between their presence/absence and virulence. Additionally, multiple known virulence genes of V. splendidus pathogens shown to be important for virulence of species were not significantly correlated with our pathogenicity results, including. Type 6 Secretion Systems (T6SS) [14, 15, 66]. Apparent differences between previous results and ours may arise from minor, but important, differences in sequences of each locus, differences in methods applied for gene clustering, environmental conditions such as temperature, or divergent controls of transcription of conserved genes between pathogenic and non-pathogenic isolates. Host factors such as age (larvae versus adult C. gigas) or a host genotype factor [67] may also account for the divergent results. An additional caveat of our work is that gene clusters detected as in-paralogs, such as one of the T6SS clusters present in many genomes of pathogens, were not analyzed with the phylogenetic logistic regression precluding direct comparisons with previous results.

While only 19 homologous gene clusters were found to be positively correlated with survival of larvae, 508 homologs were identified to negatively correlate with high survival rates, indicating that Vibrio pathogenesis towards C. gigas larvae is a complex trait involving multiple adaptive features. The wide diversity of functions encoded by genes correlated with larval pathogenicity, included ones with more classical virulence functions such as proteases and T3SS-related functions, but also genes putatively encoding functions for colonization, nutrient acquisition, and those with unknown functions. Our results indicate that infection of oyster larvae by V. coralliilyticus may involve an intracellular stage facilitated by the T3SS and the Reb defense system. We therefore hypothesize that these pathogens interact with the immune system of larvae in a similar manner that is seen for V. coralliilyticus infection of the coral Pocillopora damicornis and of V. tasmaniensis with oysters, where infection proceeds through the formation of intracellular bacterial aggregates, followed by the repression of host innate immunity, host cell lysis and extensive tissue damage [15, 68, 69]. The genetic features described here will be fruitful targets for future mechanistic studies of the oyster larvae pathogenesis.

Conclusions

This work highlights the importance of Vibrio genes associated with Pacific oyster (Crassostrea gigas) larvae mortalities. Forty-two newly sequenced hatchery-associated Vibrio genomes are presented in this study. Out of a wide range of Vibrio species, the most prevalent pathogenic species identified were V. coralliilyticus strains. Important considerations of our work are that both pathogenic and non-pathogenic vibrios were isolated from hatcheries when no disease symptoms were evident, and that vibrosis in oysters does not result from one single etiological agent or genetic feature, instead appearing to be a multifaceted trait enabled by many loci. These findings point to the complexity of vibrosis outbreaks, whereby environment, microbial community structure, and genotypic and phenotypic features of Vibrio populations all have important roles in disease outcomes. These findings have meaningful implications on monitoring and management practices of aquaculture, where multiple genetic traits and species are all relevant and must be considered with regard to controlling disease.

Methods

Pathogenicity of Vibrio isolates against C. gigas larvae

C. gigas larvae were collected 48 h post-spawn from a hatchery in Newport, Oregon. Seawater was collected from an onsite intake line and autoclaved. Isochrysis sp. was used for larvae food. Vibrio isolate strains were grown at a temperature of 25 °C (with the exception of strain 99–70-13A1, which was grown at 23 °C) in LB + 3% NaCl (LBS) to an OD600 of 0.8. Cell cultures were serially diluted in sterile seawater, and cell densities were confirmed by colony forming units (CFUs) on LBS. On the day of larvae collection, larval density was estimated by microscopy. The absence of Vibrio bacteria in unamended stocks of C. gigas larvae and algal feed was confirmed by direct plating on Thiosulfate Citrate Bile Salts Sucrose (TBCS) agar.

Challenges with Vibrio isolate strains and C. gigas larvae were performed in sterile 24-well plates. For each replicate challenge, 20–30 larvae and 105 Vibrio cells/ml in sterile seawater were added to each well at a final volume of 6 ml. Bacteria-free controls were performed with larvae alone in sterile seawater. Known pathogenic (V. coralliilyticus str. RE98) and non-pathogenic (V. cyclitrophicus HMSC5) isolates, added at d 105 Vibrio cells/ml in sterile seawater, were used in challenges as positive and negative controls, respectively. Wells were sealed with parafilm and covered during subsequent incubations at 25 °C for 48 h. The neutral red uptake assay was performed post-challenge to quantify larvae mortality. Briefly, neutral red solution was added to each well at a final concentration of 0.0002% (v/v). After 3 h, 30 μl of 10% buffered formalin was added to wells. Larvae were visualized with a dissecting microscope to characterize morphology and quantify dye uptake. Each Vibrio isolate was tested in quadruplicate, and mean mortality rates and standard deviations are reported. Each strain was tested for hemolytic and proteolytic activity. To test for extracellular proteolytic activity, strains were streaked on LB supplemented with NaCl (3%) and skim milk (2%) agar and incubated at room temperature (~ 21 °C) and 28 °C for 48 h. To test for hemolytic activity, strains were streaked on Nutrient Agar supplemented with horse blood (5%) at room temperature for 48 h.

Genome sequencing and assembly

All Vibrio strains were obtained from culture collections or isolated directly from samples collected from commercial shellfish hatcheries. Isolates from hatcheries were obtained by plating samples of intake water and larvae tanks on TCBS. Colonies characteristic of Vibrio spp. were restreaked for isolation before growth in liquid broth and cryogenic preservation at − 80 °C.

Genomic DNA from overnight cultures of isolates was purified using the phenol-chloroform extraction method [70] or a DNeasy Blood & Tissue Kits (QIAGEN, Venlo, Netherlands). Quality and quantity of isolated genomic DNA was assessed by electrophoresis on a 0.9% agarose gel, by measuring absorbance wavelengths at 260 and 280 nm, and Qubit fluorometry (Thermo Fisher Scientific, Waltham, MA USA). DNA sequencing libraries of genomic DNAs were prepared using Nextera XT kit (Illumina Inc., San Diego, CA USA). Libraries were pooled together at equal concentrations and sequenced using the Illumina Miseq Platform (2 × 250 bp) at Oregon State University’s Center for Genome Research and Biocomputing (CGRB). Raw sequencing reads were demultiplexed and barcodes removed prior to quality-filtering and trimming using Sickle. High quality reads were then randomly subsampled to obtain approximately 50-fold coverage of the genome and assembled using IDBA-UD version 1.0.6 [71] with iterative k-mer assembly between a 45 and 105 with 4-mer increments. Annotation of contigs > 500 bp was conducted with Prokka with default settings [72]. Completeness and purity of assembled genomes was assessed with the CheckM tool [73]. Average-nucleotide identity (ANI) values between all sequenced genomes and a downloaded set of reference genomes from all Vibrio species available from NCBI Genbank (date accessed: May 1st 2018) were calculated using the tool FastANI [74]. A 95% ANI cut-off ratio to reference genomes was used to putatively assign species labels to each of the newly sequenced genomes [74,75,76].

Comparative genomics and phylogenomics

Homologous clusters of gene families were identified using the GET_HOMOLOGUES software package [77] with OrthoMCL clustering [32] using default parameters. Orthologous clusters containing two or more members from the same genome (defined as inparalogs) were excluded (n = 1087) from subsequent statistical tests. The “core genome” was defined as those genes present in all 51 strains tested, while the “soft core genome” was defined as genes found in greater than 95% of the 51 genomes analyzed. The “flex genome” was defined as those genes present in 5–95% of the genomes. The Heaps law model [78] was used with the function “heaps” in the R package micropan [79] to estimate pan genome openness.

A phylogeny based on single-copy universal genes was built for the 51 tested Vibrio genomes using the “bcgTree” pipeline [80]. Briefly, the hmmer program (version 3.1b2, http://hmmer.org/) searched all predicted protein coding sequences from the Vibrio genomes against a database of hidden markov model (hmm) profiles of genes present in over 95% of bacterial genomes [81]. Best matches above a gene-specific cut-off were retained and query sequences with the same database match were aligned using MUSCLE with default settings [82]. Resulting alignments were filtered with Gblocks [83] and concatenated with AMAS [84]. RAxML was used to build a partitioned maximum likelihood phylogenetic tree from this alignment, using a GAMMA distribution of rate heterogeneity and individually estimated models of evolution for each gene partition of the concatenated alignment [85].

A phylogeny was constructed from the core genome of all newly sequenced Vibrio genomes, 98 reference Vibrio genomes, and the Enterovibrio norvegicus DSM 15893 and Photobacterium gaetbulicola Gung47 genomes as outgroups. All reference genomes downloaded as nucleotide fastas from RefSeq and annotated using the PROKKA pipeline for consistency. Genomes with an N90 under 10,000 bp, estimated to be less than 90% complete, or with high strain heterogeneity were excluded from further analysis. To define the core genome of this dataset amino acid sequences from all genes were used as input for the GET_HOMOLOGUES pipeline. OrthoMCL was used to cluster protein sequences with 50% minimum coverage, 50% minimum identity and a default e-value of 0.00001. A concatenated alignment and phylogenomic tree was then created from the core set of homologous genes as described above for the single copy, universal gene data set. The two phylogenies were then analyzed for congruency with one another using the ‘treedist’ function in the package ‘phangorn’ [86] with KF distance.

Detection of gene orthologs correlated with pathogenicity

To identify the set of genes in Vibrio genomes significantly associated with larvae mortalities, correlations between presence or absence of genes and the results of larval pathogenicity assays were calculated with a phylogenetic logistic regression model of the R package “phylolm” (86). Gene presence/absence across all genomes was considered binary response variable and percent survival was considered a predictor variable for each model, and all single-copy genes present in 3 or more genomes but not more than 46 genomes (n = 6676) were tested. The phylogeny constructed from single-copy, universal genes was used as input for the model. Gene occurrences were considered significantly correlated with larvae data using a p-value cutoff of 0.005, and the effect of the relationship was determined by the strength and sign of the correlation estimate.

Functional category analysis

Orthologous gene clusters were functionally annotated against the Non-supervised Orthologous Groups database (eggNOG) [87]. Each orthologous cluster was aligned with MUSCLE [82], and an hmm profile was built HMMER, which was then used to search against the eggNOG GAMMAPROTNOG database to provide a consistent annotation of each respective cluster. A chi-squared test was conducted to compare the distribution of functional categories for genes identified to be significantly negatively correlated with larvae survival to the distribution of functional categories for genes in the core genome. Pairwise post-hoc tests for each functional category were then performed with an FDR correction to determine which individual functional categories were significantly different between each dataset. Categories were considered enriched at a significance level of p < 0.05.

Phylogeny of T3SS genes

To infer phylogenetic relationships of specific genes, including the sctN (the Type III secretion cytoplasmic ATPase) and sctV (Type III secretion inner membrane protein) homologues, hmm profiles for genes of interest were obtained from the Pfam database [88]. These profiles were used to search against a database of putative protein sequences from the newly sequenced and reference Vibrio genomes. Best matches falling within an inclusion threshold of an E-value ≤0.01 were selected as putative homologues for downstream analyses. A multiple sequence alignment with identified homologues, as well as a database of reference proteins for sequences of interest, was created with MUSCLE [82]. A maximum-likelihood phylogenetic tree was constructed using RAxML with GAMMA model of rate heterogeneity and the LG model of protein substitution and empirical base frequencies (as determined by automated protein model assignment).

Availability of data and materials

The draft genomes of the 42 Vibrio isolates have been deposited in the GenBank database under project accession no. PRJNA563078.

Abbreviations

ANI:

Average nucleotide identity

eggNOG:

Non-supervised orthologous groups database

COG:

Clusters of orthologous groups

CPI-1:

Coralliilyticus pathogenicity island 1

KF:

Kuhner-felsenstein

MARTX:

Multifunctional-autoprocessing repeats-in-toxin

RTX:

Repeats-in-toxin

T1SS:

Type 1 secretion system

T3SS:

Type 3 secretion system

T6SS:

Type 6 secretion system

References

  1. 1.

    Nair GB. Seasonal distribution of Vibrio parahaemolyticus in freshwater environs and in association with freshwater fishes in Calcutta. Appl Env Microbiol. 1985;49:5.

    Google Scholar 

  2. 2.

    Thompson JR, Randa MA, Marcelino LA, Tomita-Mitchell A, Lim E, Polz MF. Diversity and dynamics of a North Atlantic coastal Vibrio community. Appl Environ Microbiol. 2004;70:4103–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Gradoville MR, Crump BC, Häse CC, White AE. Environmental controls of oyster-pathogenic Vibrio spp. in Oregon estuaries and a shellfish hatchery. Appl Environ Microbiol. 2018;84. https://doi.org/10.1128/AEM.02156-17.

  4. 4.

    Beaz Hidalgo R, Cleenwerck I, Balboa S, Prado S, De Vos P, Romalde JL. Vibrio breoganii sp. nov., a non-motile, alginolytic, marine bacterium within the Vibrio halioticoli clade. Int J Syst Evol Microbiol. 2009;59:1589–94.

    PubMed  Google Scholar 

  5. 5.

    Chonsin K, Matsuda S, Theethakaew C, Kodama T, Junjhon J, Suzuki Y, et al. Genetic diversity of Vibrio parahaemolyticus strains isolated from farmed Pacific white shrimp and ambient pond water affected by acute hepatopancreatic necrosis disease outbreak in Thailand. FEMS Microbiol Lett. 2016;363:fnv222.

    PubMed  Google Scholar 

  6. 6.

    Alam M, Sultana M, Nair GB, Siddique AK, Hasan NA, Sack RB, et al. Viable but nonculturable Vibrio cholerae O1 in biofilms in the aquatic environment and their role in cholera transmission. Proc Natl Acad Sci. 2007;104:17801–6.

    CAS  PubMed  Google Scholar 

  7. 7.

    Yawata Y, Cordero OX, Menolascina F, Hehemann J-H, Polz MF, Stocker R. Competition-dispersal tradeoff ecologically differentiates recently speciated marine bacterioplankton populations. Proc Natl Acad Sci. 2014;111:5622–7.

    CAS  PubMed  Google Scholar 

  8. 8.

    Toranzo AE, Magariños B, Romalde JL. A review of the main bacterial fish diseases in mariculture systems. Aquaculture. 2005;246:37–61.

    Google Scholar 

  9. 9.

    Elston R, Leibovitz L, Relyea D, Zatila J. Diagnosis of vibriosis in a commercial oyster hatchery epizootic: diagnostic tools and management features. Aquaculture. 1981;24:53–62.

    Google Scholar 

  10. 10.

    Prado S, Romalde J, Montes J, Barja J. Pathogenic bacteria isolated from disease outbreaks in shellfish hatcheries. First description of Vibrio neptunius as an oyster pathogen. Dis Aquat Org. 2005;67:209–15.

    CAS  PubMed  Google Scholar 

  11. 11.

    Elston R, Hasegawa H, Humphrey K, Polyak I, Häse C. Re-emergence of Vibrio tubiashii in bivalve shellfish aquaculture: severity, environmental drivers, geographic extent and management. Dis Aquat Org. 2008;82:119–34.

    PubMed  Google Scholar 

  12. 12.

    Prado S, Dubert J, Barja JL. Characterization of pathogenic vibrios isolated from bivalve hatcheries in Galicia, NW Atlantic coast of Spain. Description of Vibrio tubiashii subsp. europaensis subsp. nov. Syst Appl Microbiol. 2015;38:26–9.

    CAS  PubMed  Google Scholar 

  13. 13.

    Bruto M, James A, Petton B, Labreuche Y, Chenivesse S, Alunno-Bruscia M, et al. Vibrio crassostreae, a benign oyster colonizer turned into a pathogen after plasmid acquisition. ISME J. 2017;11:1043–52.

    PubMed  Google Scholar 

  14. 14.

    Bruto M, Labreuche Y, James A, Piel D, Chenivesse S, Petton B, et al. Ancestral gene acquisition as the key to virulence potential in environmental Vibrio populations. ISME J. 2018;12:2954–66.

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Rubio T, Oyanedel D, Labreuche Y, Toulza E, Luo X, Bruto M, et al. Species-specific mechanisms of cytotoxicity toward immune cells determine the successful outcome of Vibrio infections. Proc Natl Acad Sci. 2019;116:14238–47.

    CAS  PubMed  Google Scholar 

  16. 16.

    Binesse J, Delsert C, Saulnier D, Champomier-Verges M-C, Zagorec M, Munier-Lehmann H, et al. Metalloprotease Vsm is the major determinant of toxicity for extracellular products of Vibrio splendidus. Appl Environ Microbiol. 2008;74:7108–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Labreuche Y, Le Roux F, Henry J, Zatylny C, Huvet A, Lambert C, et al. Vibrio aestuarianus zinc metalloprotease causes lethality in the Pacific oyster Crassostrea gigas and impairs the host cellular immune defenses. Fish Shellfish Immunol. 2010;29:753–8.

    CAS  PubMed  Google Scholar 

  18. 18.

    Hasegawa H, Lind EJ, Boin MA, Hase CC. The extracellular Metalloprotease of Vibrio tubiashii is a major virulence factor for Pacific oyster (Crassostrea gigas) larvae. Appl Environ Microbiol. 2008;74:4101–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Ben-Haim Y, Rosenberg E. A novel Vibrio sp. pathogen of the coral Pocillopora damicornis. Mar Biol. 2002;141:47–55.

    Google Scholar 

  20. 20.

    Ben-Haim Y. Vibrio coralliilyticus sp. nov., a temperature-dependent pathogen of the coral Pocillopora damicornis. Int J Syst Evol Microbiol. 2003;53:309–15.

    CAS  PubMed  Google Scholar 

  21. 21.

    Ushijima B, Videau P, Burger AH, Shore-Maggio A, Runyon CM, Sudek M, et al. Vibrio coralliilyticus strain OCN008 is an etiological agent of acute Montipora White syndrome. Appl Environ Microbiol. 2014;80:2102–9.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Tubiash HS, Chanley PE, Leifson E. Bacillary necrosis, a disease of larval and juvenile bivalve mollusks. J Bacteriol. 1965;90:9.

    Google Scholar 

  23. 23.

    Estes R, Friedman C, Elston R, Herwig R. Pathogenicity testing of shellfish hatchery bacterial isolates on Pacific oyster Crassostrea gigas larvae. Dis Aquat Org. 2004;58:223–30.

    PubMed  Google Scholar 

  24. 24.

    Gómez-León J, Villamil L, Salger S, Sallum R, Remacha-Triviño A, Leavitt D, et al. Survival of eastern oysters Crassostrea virginica from three lines following experimental challenge with bacterial pathogens. Dis Aquat Org. 2008;79:95–105.

    PubMed  Google Scholar 

  25. 25.

    Richards GP, Watson MA, Needleman DS, Church KM, Häse CC. Mortalities of eastern and Pacific oyster larvae caused by the pathogens Vibrio coralliilyticus and Vibrio tubiashii. Appl Environ Microbiol. 2015;81:292–7.

    PubMed  Google Scholar 

  26. 26.

    Genard B, Miner P, Nicolas J-L, Moraga D, Boudry P, Pernet F, et al. Integrative study of physiological changes associated with bacterial infection in Pacific oyster larvae. PLoS One. 2013;8:e64534.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Ushijima B, Richards GP, Watson MA, Schubiger CB, Häse CC. Factors affecting infection of corals and larval oysters by Vibrio coralliilyticus. PLoS One. 2018;13:e0199475.

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Kehlet-Delgado H, Richards GP, Häse C, Mueller RS. Three draft genome sequences of Vibrio coralliilyticus strains isolated from bivalve hatcheries. Genome Announc. 2017;5:e01162–17.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Ushijima B, Videau P, Poscablo D, Stengel JW, Beurmann S, Burger AH, et al. Mutation of the toxR or mshA genes from Vibrio coralliilyticus strain OCN014 reduces infection of the coral Acropora cytherea: virulence genes in Vibrio coralliilyticus. Environ Microbiol. 2016;18:4055–67.

    CAS  PubMed  Google Scholar 

  30. 30.

    Gharaibeh DN, Hasegawa H, Häse CC. Development of a quantitative real-time PCR assay for detection of Vibrio tubiashii targeting the metalloprotease gene. J Microbiol Methods. 2009;76:262–8.

    CAS  PubMed  Google Scholar 

  31. 31.

    Kuhner M, Felsenstein J. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol Biol Evol. 1994. https://doi.org/10.1093/oxfordjournals.molbev.a040126.

  32. 32.

    Li L. OrthoMCL: identification of Ortholog groups for eukaryotic genomes. Genome Res. 2003;13:2178–89.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Kimes NE, Grim CJ, Johnson WR, Hasan NA, Tall BD, Kothary MH, et al. Temperature regulation of virulence factors in the pathogen Vibrio coralliilyticus. ISME J. 2012;6:835–46.

    CAS  PubMed  Google Scholar 

  34. 34.

    Dobrindt U, Hochhut B, Hentschel U, Hacker J. Genomic islands in pathogenic and environmental microorganisms. Nat Rev Microbiol. 2004;2:414–24.

    CAS  PubMed  Google Scholar 

  35. 35.

    Zhang L, Krachler AM, Broberg CA, Li Y, Mirzaei H, Gilpin CJ, et al. Type III effector VopC mediates invasion for Vibrio species. Cell Rep. 2012;1:453–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Hu Y, Huang H, Cheng X, Shu X, White AP, Stavrinides J, et al. A global survey of bacterial type III secretion systems and their effectors: a global survey of bacterial type III secretion systems. Environ Microbiol. 2017;19:3879–95.

    CAS  PubMed  Google Scholar 

  37. 37.

    Jeong H-G, Satchell KJF. Additive function of Vibrio vulnificus MARTXVv and VvhA Cytolysins promotes rapid growth and epithelial tissue necrosis during intestinal infection. PLoS Pathog. 2012;8:e1002581.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Kothary MH, Delston RB, Curtis SK, McCardell BA, Tall BD. Purification and characterization of a Vulnificolysin-like Cytolysin produced by Vibrio tubiashii. Appl Environ Microbiol. 2001;67:3707–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Song X, Wang H, Xin L, Xu J, Jia Z, Wang L, et al. The immunological capacity in the larvae of Pacific oyster Crassostrea gigas. Fish Shellfish Immunol. 2016;49:461–9.

    CAS  PubMed  Google Scholar 

  40. 40.

    Allam B, Paillard C. Defense factors in clam extrapallial fluids. Dis Aquat Org. 1998;33:123–8.

    Google Scholar 

  41. 41.

    Allam B, Pales EE. Bivalve immunity and response to infections: are we looking at the right place? Fish Shellfish Immunol. 2016;53:4–12.

    CAS  PubMed  Google Scholar 

  42. 42.

    Canesi L, Scarpato A, Betti M, Ciacci C, Pruzzo C, Gallo G. Bacterial killing by mytilus hemocyte monolayers as a model for investigating the signaling pathways involved in mussel immune defence. Mar Environ Res. 2002;54:547–51.

    CAS  PubMed  Google Scholar 

  43. 43.

    Schmitt P, Rosa RD, Duperthuy M, de Lorgeril J, Bachère E, Destoumieux-Garzón D. The antimicrobial defense of the pacific oyster, crassostrea gigas. How diversity may compensate for scarcity in the regulation of resident/pathogenic microflora. Front Microbiol. 2012;3. https://doi.org/10.3389/fmicb.2012.00160.

  44. 44.

    Modak T. Eastern oyster larval Transcriptomes in response to probiotic and pathogenic Bacteria. PhD Thesis. University of Rhode Island; 2018.

  45. 45.

    Pond FR, Gibson I, Lalucat J, Quackenbush RL. R-body-producing bacteria. Microbiol Mol Biol Rev. 1989;53:25–67.

    CAS  Google Scholar 

  46. 46.

    Polka JK, Silver PA. A tunable protein piston that breaks membranes to release encapsulated cargo. ACS Synth Biol. 2016;5:303–11.

    CAS  PubMed  Google Scholar 

  47. 47.

    Goudenège D, Labreuche Y, Krin E, Ansquer D, Mangenot S, Calteau A, et al. Comparative genomics of pathogenic lineages of Vibrio nigripulchritudo identifies virulence-associated traits. ISME J. 2013;7:1985–96.

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Raymann K, Bobay L-M, Doak TG, Lynch M, Gribaldo S. A genomic survey of reb homologs suggests widespread occurrence of R-bodies in proteobacteria. G3amp58 GenesGenomesGenetics. 2013;3:505–16.

    CAS  Google Scholar 

  49. 49.

    Manske C, Schell U, Hilbi H. Metabolism of myo -inositol by legionella pneumophila promotes infection of amoebae and macrophages. Appl Environ Microbiol. 2016;82:5000–14.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Zhang F, Hu B, Fu H, Jiao Z, Li Q, Liu S. Comparative Transcriptome analysis reveals molecular basis underlying fast growth of the selectively bred Pacific oyster, Crassostrea gigas. Front Genet. 2019;10:610.

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Malagoli D, Franchini A, Ottaviani E. Synergistic role of cAMP and IP3 in corticotropin-releasing hormone-induced cell shape changes in invertebrate immunocytes. Peptides. 2000;21:175–82.

    CAS  PubMed  Google Scholar 

  52. 52.

    Wang Z, Wang B, Chen G, Jian J, Lu Y, Xu Y, et al. Transcriptome analysis of the pearl oyster ( Pinctada fucata ) hemocytes in response to Vibrio alginolyticus infection. Gene. 2016;575:421–8.

    CAS  PubMed  Google Scholar 

  53. 53.

    Lin H, Yu M, Wang X, Zhang X-H. Comparative genomic analysis reveals the evolution and environmental adaptation strategies of vibrios. BMC Genomics. 2018;19. https://doi.org/10.1186/s12864-018-4531-2.

  54. 54.

    Kirn TJ, Jude BA, Taylor RK. A colonization factor links Vibrio cholerae environmental survival and human infection. Nature. 2005;438:863–6.

    CAS  PubMed  Google Scholar 

  55. 55.

    Hunt DE, Gevers D, Vahora NM, Polz MF. Conservation of the chitin utilization pathway in the Vibrionaceae. Appl Environ Microbiol. 2008;74:44–51.

    CAS  PubMed  Google Scholar 

  56. 56.

    Mondal M, Nag D, Koley H, Saha DR, Chatterjee NS. The Vibrio cholerae extracellular Chitinase ChiA2 is important for survival and pathogenesis in the host intestine. PLoS One. 2014;9:e103119.

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Svitil AL. Chitin degradation proteins produced by the marine bacterium Vibrio harveyi growing on different forms of chitin. Appl Env Microbiol. 1997;63:6.

    Google Scholar 

  58. 58.

    Meibom KL. Chitin induces natural competence in Vibrio cholerae. Science. 2005;310:1824–7.

    CAS  PubMed  Google Scholar 

  59. 59.

    Suzuki M, Sakuda S, Nagasawa H. Identification of chitin in the prismatic layer of the Shell and a chitin synthase gene from the Japanese pearl oyster, Pinctada fucata. Biosci Biotechnol Biochem. 2007;71:1735–44.

    CAS  PubMed  Google Scholar 

  60. 60.

    Zhang G, Fang X, Guo X, Li L, Luo R, Xu F, et al. The oyster genome reveals stress adaptation and complexity of shell formation. Nature. 2012;490:49–54.

    CAS  PubMed  Google Scholar 

  61. 61.

    Ivanina AV, Falfushynska HI, Beniash E, Piontkivska H, Sokolova IM. Biomineralization-related specialization of hemocytes and mantle tissues of the Pacific oyster Crassostrea gigas. J Exp Biol. 2017;220:3209–21.

    PubMed  Google Scholar 

  62. 62.

    Giubergia S, Phippen C, Nielsen KF, Gram L. Growth on chitin impacts the transcriptome and metabolite profiles of antibiotic-producing Vibrio coralliilyticus S2052 and Photobacterium galatheae S2753. mSystems. 2017;2:e00141–16 /msys/2/1/e00141–16.atom.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Johnson CN. Fitness factors in Vibrios: a mini-review. Microb Ecol. 2013;65:826–51.

    CAS  PubMed  Google Scholar 

  64. 64.

    de O Santos E, Alves N, Dias GM, Mazotto AM, Vermelho A, Vora GJ, et al. Genomic and proteomic analyses of the coral pathogen Vibrio coralliilyticus reveal a diverse virulence repertoire. ISME J. 2011;5:1471–83.

    Google Scholar 

  65. 65.

    Hasegawa H, Hase CC. TetR-type transcriptional regulator VtpR functions as a global regulator in Vibrio tubiashii. Appl Environ Microbiol. 2009;75:7602–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Piel D, Bruto M, James A, Labreuche Y, Lambert C, Janicot A, et al. Selection of Vibrio crassostreae relies on a plasmid expressing a type 6 secretion system cytotoxic for host immune cells. Environ Microbiol. 2019. https://doi.org/10.1111/1462-2920.14776.

  67. 67.

    Wendling CC, Wegner KM. Adaptation to enemy shifts: rapid resistance evolution to local Vibrio spp. in invasive Pacific oysters. Proc R Soc B Biol Sci. 2015;282:20142244.

    Google Scholar 

  68. 68.

    Vidal-Dupiol J, Ladriere O, Meistertzheim A-L, Foure L, Adjeroud M, Mitta G. Physiological responses of the scleractinian coral Pocillopora damicornis to bacterial stress from Vibrio coralliilyticus. J Exp Biol. 2011;214:1533–45.

    CAS  PubMed  Google Scholar 

  69. 69.

    Vidal-Dupiol J, Dheilly NM, Rondon R, Grunau C, Cosseau C, Smith KM, et al. Thermal stress triggers broad Pocillopora damicornis Transcriptomic remodeling, while Vibrio coralliilyticus infection induces a more targeted Immuno-suppression response. PLoS One. 2014;9:e107672.

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Crump BC, Kling GW, Bahr M, Hobbie JE. Bacterioplankton community shifts in an Arctic Lake correlate with seasonal changes in organic matter source. Appl Environ Microbiol. 2003;69:2253–68.

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28:1420–8.

    CAS  PubMed  Google Scholar 

  72. 72.

    Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.

    CAS  PubMed  Google Scholar 

  73. 73.

    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9. https://doi.org/10.1038/s41467-018-07641-9.

  75. 75.

    Konstantinidis KT, Tiedje JM. Genomic insights that advance the species definition for prokaryotes. Proc Natl Acad Sci. 2005;102:2567–72.

    CAS  PubMed  Google Scholar 

  76. 76.

    Goris J, Konstantinidis KT, Klappenbach JA, Vandamme P, Coenye T, Tiedje JM. DNA–DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol. 2007;57:81–91.

    CAS  PubMed  Google Scholar 

  77. 77.

    Contreras-Moreira B, Vinuesa P. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial Pangenome analysis. Appl Environ Microbiol. 2013;79:7696–701.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Tettelin H, Riley D, Cattuto C, Medini D. Comparative genomics: the bacterial pan-genome. Curr Opin Microbiol. 2008;11:472–7.

    CAS  PubMed  Google Scholar 

  79. 79.

    Snipen L, Liland KH. micropan: an R-package for microbial pan-genomics. BMC Bioinformatics. 2015;16. https://doi.org/10.1186/s12859-015-0517-0.

  80. 80.

    Ankenbrand MJ, Keller A. bcgTree: automatized phylogenetic tree building from bacterial core genomes. Genome. 2016;59:783–91.

    CAS  PubMed  Google Scholar 

  81. 81.

    Dupont CL, Rusch DB, Yooseph S, Lombardo M-J, Alexander Richter R, Valas R, et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 2012;6:1186–99.

    CAS  PubMed  Google Scholar 

  82. 82.

    Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol. 2000;17:540–52.

    CAS  PubMed  Google Scholar 

  84. 84.

    Borowiec ML. AMAS: a fast tool for alignment manipulation and computing of summary statistics. PeerJ. 2016;4:e1660.

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Liu K, Linder CR, Warnow T. RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation. PLoS One. 2011;6:e27731.

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Schliep KP. Phangorn: phylogenetic analysis in R. Bioinformatics. 2011;27:592–3.

    CAS  PubMed  Google Scholar 

  87. 87.

    Powell S, Szklarczyk D, Trachana K, Roth A, Kuhn M, Muller J, et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 2012;40:D284–9.

    CAS  PubMed  Google Scholar 

  88. 88.

    El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–32.

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Ralph Elston and Gary Richards for providing several isolates used in this study, Blake Ushijima and Carla Schubiger for technical assistance, and David Madison for providing oyster larvae and advice in virulence assays.

Funding

This work was supported by Special Research Grants Program - Aquaculture Research, grant number: 2015–70007-24240 / project accession no. 1007649) from the USDA National Institute of Food and Agriculture. HD was a recipient of the OSU Tartar Fellowship and John Fryer Scholarship. The funding bodies were not involved in design of the study and collection, analysis, and interpretation of results or in writing the manuscript.

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HD performed wet-lab experiments, analysed the data, and drafted the manuscript. RSM and CH supervised the study and revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Hanna Kehlet-Delgado.

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Supplementary information

Additional file 1 Table S1

. Hemolysis and proteolysis reactions.

Additional file 2 Fig. S1.

Heatmap and dendrogram of Average Nucleotide Identity (ANI) calculations between pairs of different Vibrio strains, including strains used in this study and reference strains. ANI of ≥95% with a Type Strain of a species was used for species designation of newly sequenced strains. Strains without ANI ≥ 95% with any Type Strain were designated “sp.”

Additional file 3 Fig. S2.

Phylogenetic Tree of strains used in this study and reference strains. A concatenated alignment of 686 conserved amino acid sequences from single copy orthologous genes was used to construct a maximum likelyhood phylogeny.

Additional file 4 Fig. S3.

Core and Pan-genome of 51 isolates in this study. (A) the core-genome represents all genes shared by each genome added. (B) The pan-genome represents the accumulation of all genes among all genomes with the addition of each genome.

Additional file 5 Table S2.

Phylogenetic logistic regression results.

Additional file 6 Table S3.

Metabolism and nutrient acquisition genes significantly correlated with larvae survival/mortality.

Additional file 7 Fig. S4.

Count distributions of bactNOG functional categories of genes among categories of different gene sets. The “Core in All” column refers to the core genome (n = 1693 gene clusters) of all strains (n = 51). The “Core in Non-Pathogens” column refers to the core genome (n = 1775 gene clusters) of all strains (n = 34) that are non-pathogenic (0.5 or lower mortality ratio). The “Core in Pathogens” column refers to the core genome (n = 2646 gene clusters) of pathogenic strains. The “Exclusive to Non-Pathogens” column refers to genes (n = 4010 gene clusters) that are found only in non-pathogens, but not necessarily conserved. The “Exclusive to Pathogens” column refers to genes (n = 1981 gene clusters) that are found only in pathogens, but not necessarily conserved. The “Flexible” column refers to genes (n = 3345 gene clusters) that are found at least once in both pathogens and non-pathogens, but are not conserved in either. The “Phyloglm Significant with Mortalities” column refers to all genes (n = 509 gene clusters) that had a significant correlation with pathogenicity towards C. gigas larvae.

Additional file 8 Fig. S5.

Gene organization of the CPI-1 region within the complete genome of V. coralliilyticus RE22 and the draft genome of V. pectenicida 99–46-Y. Pink arrows indicate that the gene had a negative correlation with larval survival in virulence assays. Blue arrows indicate that there was no significant correlation with survival or that the gene was not tested. Yellow slashed lines indicate contig breaks on the CPI-1 of the V. pectenicida strain 99–46-Y draft genome.

Additional file 9 Fig. S6.

Predicted phylogeny of Type 3 Secretion Systems using SctV amino acid sequences. Clades are color coded. Included in the phylogeny are proteins from tested isolates, specific Vibrio reference genomes, and from COG4789 comprised of more diverse reference genomes. Scale bar represents one change per amino acid. Tree was visualized with iTOL.

Additional file 10 Table S4.

Pan-genome table (excluding genes only found once in a genome) with strain RE22 for reference loci.

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Kehlet-Delgado, H., Häse, C.C. & Mueller, R.S. Comparative genomic analysis of Vibrios yields insights into genes associated with virulence towards C. gigas larvae. BMC Genomics 21, 599 (2020). https://doi.org/10.1186/s12864-020-06980-6

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Keywords

  • Vibrio
  • Aquaculture
  • Comparative genomics
  • Oyster larvae
  • Vibrio coralliilyticus
  • Vibriosis
  • Crassostrea gigas
  • Prokaryotic genomics