Dysbiosis in epizootic shell disease of the American lobster (Homarus americanus).
KEY WORDS: American lobster, Homarus americanus, epizootic shell disease, bacteria, dysbiosis
Epizootic shell disease (ESD) represents an economic problem for fishermen fishing American lobsters (Homarus americanus, Milne Edwards) in the southern New England (SN E) stock, and may indicate an ecological crisis for the lobster and other members of the benthic community in the region. The histopathology of ESD has been described by Smolowitz et al. (2005). The presence of lesions on the carapace of an infected lobster is the most visible sign of the disease. Histological examination revealed bacteria as the most common organisms in the lesions, with some protist constituents in more advanced cases. The lesions were grouped into 3 categories based on depth of the bacterial incursion, presuming that depth reflects a progressive erosion of the cuticle.
Category 1 is the least severe erosion, with shallow lesions extending into the epicuticle and exocuticle. The lateral edges of the lesions often exhibited evidence of melanization, but inflammation in the underlying connective tissue or other evidence of an immune response is rarely observed at this stage. Bacteria are found in the leading edges of the lesion, and in thecrystalline chitin lattice.
Category 2 lesions are moderately deep, penetrating the calcified endocuticle. The crystalline lattice structure of the chitin take on a "pillarlike" appearance as the bacteria degrade the protein structure between the lattice crystals. The endocuticle exhibits melanization, especially in the vertical areas of bacterial incursion. Evidence of an immune response to the infection includes inflammation of the underlying cuticular epithelium and "moderate numbers" of hemocytes in the tissues. Secondary invasion by small protists occurs during this stage. These organisms are apparently responsible for the degradation of the crystal lattice structure of the chitin. In some category 2 lesions, an "inflammatory cuticle" forms between the uncalcified endocuticle and the cuticular epithelium. The latter has some areas of hyperplasia and hypertrophy.
Deeper erosions into the uncalcified endocuticle are characterized as category 3 lesions. At this stage, the overlying structures of the carapace are absent, and the exposed areas are melanized. The cuticular epithelium is hyperplastic, hypertrophic, and intensely inflamed, with an increased number of hemocytes. The underlying connective tissue also exhibits signs of inflammation and immune response. The most extreme types of category 3 lesions have no more tissue than the inflammatory cuticle overlying the cuticular epithelium. In some cases, the lesions progress to ulcerations, which are characterized by a complete absence of cuticular tissue and cuticular epithelium. Degranulated hemocytes develop a pseudomembrane to cover the connective tissue. The outer layer of the pseudomembrane is necrotic and melanized.
Chistoserdov et al. (2005) isolated a novel chitinolytic bacterium in the genus Aquimarina (family Flavobacteriaceae), and identified it as a possible etiological agent in ESD lesions. Tlusty and Metzler (2012) discussed the physical appearance of prelesion formation whereas Quinn et al. (2012) identified Aquimarina 'homaria' as present in these prelesion initial stages of the disease. Given that bacteria and bacterial erosion, or necrosis of the cuticle are components of ESD, we undertook an analysis of the bacterial community of lesioned areas and compared them with unaffected areas to assess the correlation of various taxa in the microbial biofilm of diseased lobsters.
Cuticle samples (0.5 [cm.sup.2]) were harvested as part of the "100 Lobsters" Project (Shields et al. 2012). The lobsters were collected from within Narragansett Bay, RI. A total of 55 lobsters had ESD whereas 47 were categorized as apparently healthy (i.e. the lobsters did not have visible ESD lesions). For this study, we did not subdivide the diseased samples into the disease categories described earlier (Smolowitz et al. 2005). The samples were shipped on dry ice and stored at -80[degrees]C until processed. Three types of carapace samples were taken: those with lesions (diseased), those from lobsters with disease but were in areas free of lesions (healthy on diseased), and those from apparently healthy lobsters (healthy). The carapace samples were dissolved in EDTA and proteinase K to isolate all microorganisms from the surface and subsurface, and total DNA was extracted using the FastPrep Biol01 kit (Qbiogene/ MP Biomedicals LLC, Solon, OH). Polymerase chain reaction (PCR) was used to amplify the bacterial genes from the first 2 hypervariable regions of the 16s ribosomal RNA using universal primers 27F (5'-AGA GTT TGA TCM TGG CTC AG-Y) and 355R (5'-GCT GCC TCC CGT AGG AGT-3') (InvitrogenTM Corporation, Carlsbad, CA). Length heterogeneity PCR (LHPCR) fingerprinting (Suzuki et al. 1998) was used to survey samples rapidly and standardize the community amplification. Duplicate fingerprints were performed from each DNA extraction to verify that the community amplification was reproducible. An initial survey of various regions of the lobster carapace was done using LH-PCR, and the microbial community on the cephalothorax region was relatively uniform in healthy lobsters. Because the cephalothorax is prone to the development of lesions in diseased lobsters, it was used in all subsequent analysis.
We used multitag pyrosequencing (MTPS) (Gillevet 2006) to characterize the taxa in the microbiome of the carapace samples. We generated a set of 96 fusion primers that contained emulsion PCR linkers (454 Life Sciences) and different 7-base "barcodes" on either 27F or 355R universal 16S rRNA primers. Each sample of lobster DNA was then amplified with a unique set of tagged forward and reverse 16S rRNA primers, pooled, subjected to emulsion PCR, and pyrosequenced using a GS-FLX pyrosequencer per the manufacturer's instructions (Roche, Branchburg, N J). Data from each pooled sample were "deconvoluted" by sorting the sequences into bins based on the barcodes using custom PERL scripts. This technique allows the rapid sequencing of multiple samples at one time, yielding thousands of sequence reads per sample. The sequence reads were identified using the Bayesian analysis in the Ribosomal Database Project (Cole et al. 2009). We used a custom PERL script to calculate the normalized abundance of each taxa in a sample based on the total reads in that sample.
The sequence for A. 'homaria' (Chistoserdov et al. 2005) was not available for analysis. However, we aligned all the sequences that were identified as Aquimarina spp. by the RDP10 analysis, with reference sequences available in GenBank, and constructed a neighbor joining tree for the genus to observe the clustering of the various species and to determine whether a correlation exists between species of the genus and the apparent health status of the lobster.
We used the software program Quantitative Insights into Microbial Ecology (QIIME) (Caporaso et al. 2010) to compare the microbial community sequences derived by MTPS on the 3 types of carapace samples, and constructed a Unifrac neighbor joining tree (Lozupone & Knight 2005) that displayed graphically the similarities in the microbiome structure between each lobster sample in the study. We used PASW v.18 (IBM, Chicago, IL) for the discriminant analysis (DA) using the default parameters. Principle coordinate analysis was performed using the MultiVariate Statistical Package (Kovach Computing Services, Anglesey, Wales) using a Bray-Curtis distance metric.
We initially surveyed the lobster carapace to determine the bacterial community distribution of the cephalothorax, claws, abdomen, and tail. Figure 1 is a histogram of normalized abundance of operational taxonomic units (OTUs) that were present on the various regions of the carapace. The LH-PCR profiles revealed that, with a few exceptions, the OTUs present on the cephalothorax are representative of the community diversity present on other regions, such as the claw and the abdomen. However, the community diversity at the other sites was less than that on the cephalothorax, which may reflect the fact that the lobster cannot clean the carapace on the upper cephalothorax.
We then focused the investigation on the cephalothorax, because this is where the majority of the lesions occur in the disease. MTPS was performed on 102 lobster samples, and the resulting sequence reads were sorted based on their tags or barcodes. The analysis yielded 212,019 reads with an average of 1,594 reads per sample. Rarefaction analysis indicated that all samples were close to saturation. We identified 170 bacteria present on the cuticle of lobsters from Narragansett Bay, RI, having culled taxa that were of insufficient abundance to be statistically significant (<1%). Of these 170 bacteria, 167 were identified to the level of genus, 1 was identified to the level of family, and 2 were identified as OTUs with complete identities that were unknown. Figure 2 is a histogram of average abundance of the taxa from each disease state sample rank ordered by the normalized abundance of data from the diseased lobsters. The entire histogram for all 170 taxa is depicted in the insert to Figure 2. The most abundant genera found in the carapace microbiome include Jannaschia, Aquimarina, Cardiobacterium, Thalassobius, Micrococcineae, and Loktanella. However, essentially all of these were found in all disease classes.
Figure 3 is the Principal Coordinate Analysis (PCO) of the normalized abundance of the genera identified in each disease class using a Bray-Curtis distance metric. One can see clustering of the diseased samples (red dots), which is somewhat distinct from the healthy (yellow) and healthy-on-disease samples (blue). It should be noted that the disease clusters overlap substantially, and some of the samples from one disease class are mixed in with the samples from a different disease class. For example, a few samples from healthy lobsters are clearly within the cluster of disease samples, and there is an area of overlap among the 3 categories in the center of the chart.
A weighted Unifrac tree, built by comparing phylogenetic trees constructed using taxa richness and their corresponding abundance for each sample (Lozupone & Knight 2005), is displayed in Figure 4. The clustering of the weighted Unifrac tree indicates the similarity and abundance of the taxa in each of the samples. This figure reveals that although there is some clustering, there is no clear separation among the 3 disease classes. That is, they are distributed more or less equally throughout the neighbor joining tree. In addition, QIIME revealed that abundance levels of alphaproteobacteria, gammaproteobacteria, and Bacteroidetes levels were elevated when comparing apparently healthy lobsters with diseased lobsters, whereas Flavobacteria (the class that includes Aquimarina spp.) were less abundant in the diseased state.
The abundance data for the 102 lobster samples and 170 variables (taxa) were analyzed by DA. Three analyses were conducted for this study: the first compared cuticle from diseased versus apparently healthy animals, the second compared cuticle from diseased animals versus healthy cuticle on diseased animals, and the third compared cuticle from apparently healthy animals versus healthy cuticle on diseased animals.
The DA for the bacterial flora on cuticle from diseased versus apparently healthy lobsters used a tolerance test to eliminated 112 variables because they lacked variance between groups and, thus, did not contribute significantly to the discriminant function (i.e., the analysis between the 2 groups relied on 58 variables (bacterial genera)). The functions at the group centroids (i.e., a multivariate equivalent to the mean) were 1.463 for disease class 1 (diseased) and -1.1712 for disease class 2 (healthy). These scores demonstrate that the centroids were well separated, which indicates that the function was discriminating between the classes ([R.sub.c.sup.2] = 0.848, with Wilks' [lambda]. = 0.281, chisquare = 91.287, and P = 0.002, dr = 56) (Garson 2008).
The classification results (confusion matrix) of the first analysis are displayed in Table 1. Table 2 displays the structure coefficient table. The structure coefficients are full coefficients, meaning that they are pooled, within-groups correlations between the independent variable and the standardized canonical discriminant coefficients (Garson 2008). According to Garson (2008) and Klecka (1980), the structure coefficient is the authoritative coefficient for determining the importance of the independent variable to the discriminant function. As with the standardized canonical discriminant coefficient, the absolute value represents the quantity, and the sign indicates the equality of the correlation. In the case of structure coefficients, however, the largest absolute value is 1.0. The bacteria Aquimarina spp. had a structure coefficient of 0.268, indicating that it had a weak correlation with the function, and it ranked second to the genus Jannaschia, which had a structure coefficient of 0.325, indicating that Jannaschia correlates more strongly with the disease state. However, these correlations are relatively low, indicating that neither is correlated strongly with the disease state.
For the second comparison, the bacterial flora on the cuticle of diseased lobsters versus healthy cuticle on diseased lobsters, the functions at the group centroids were well separated, with a value of 2.060 for disease class 1 (diseased) and 3.541 for disease class 3 (healthy on diseased; [R.sub.c.sup.2] 0.939, Wilks' [lambda] = 0.118, chi-square = 131.382, P < 0.001, df= 47). The classification results of the second analysis are displayed in Table 3. The second section of Table 2 is the structure coefficient table for this analysis. These tables have been arranged to display descending order of importance, with the positive coefficients listed first. The negative coefficients appear to be listed in ascending order because the absolute value is indicative of a stronger correlation. Species in the genus Jannaschia had roughly the same structure coefficient as in the previous analysis. Species in the genus Aquimarina had a structure coefficient of 0.156, and its relative ranking dropped from second to fourth in the structure coefficient table. This indicates that Aquimarina spp. appears to contribute less to the discrimination of these 2 states than they do to discriminating between the healthy and diseased states.
For the third comparison, a comparison of the bacterial flora on the cuticle of apparently healthy lobsters versus healthy cuticle on diseased lobsters, the discriminant functions at the group centroids demonstrated good separation, with values of 2.439 for disease class 2 (healthy) and 3.582 for disease class 3 (healthy on diseased; [R.sub.c.sup.2] = 0.948, Wilks' [lambda] = 0.100, chi-square = 122.991, P < 0.001, dr= 47). The classification results of this analysis are displayed in Table 4. The third section of Table 2 contains the structure coefficient table for this comparison. In this analysis, Aquimarina spp. have the lowest structure coefficient of-0.002, indicating that it correlated weakly and negatively in discriminating between carapaces from lobsters that showed no signs of the disease and carapace samples without lesions that were taken from lobsters that have the disease.
Because the Aquimarina spp. belong to 1 of the 2 genera that had weak correlations in the previous analysis with the disease, we investigated further the species from this taxa. We aligned the sequences that were identified as Aquimarina spp. in GenBank and constructed a neighbor-joining tree (Fig. 5). A vast majority of the community sequences identified as Aquimarina spp. clustered as 3 clades that were separate from the reference Aquimarina spp. from GenBank. The sequences from apparently healthy, healthy on diseased (HD), and diseased lobsters (D) were present in all 3 clades. There were only a few sequences from apparently healthy and diseased lobsters that clustered with the reference sequences, indicating that the majority of the sequences represented novel species or strains.
We investigated the bacterial flora on the cuticle of lobsters with and without signs of ESD. The bacterial community was analyzed using PCO, Unifrac, and DA. For lobsters that had the disease versus apparently healthy animals from the same region, 58 genera of bacteria were sufficiently represented in the community to analyze their potential contribution in discriminating the disease state. However, according to the structure coefficient tables, no species of bacteria makes a substantial contribution to the discrimination function. The genera with the largest coefficients, Jannaschia spp. and Aquimarina spp., had weakly positive coefficients associated with the disease state (structure coefficient = 0.325 and 0.268, respectively). That is, ESD lesions do not appear to be correlated with any one species. Quinn et al. (2012) demonstrated that histologically similar lesions to the ones seen in ESD could be produced on lobsters when their carapace was abraded and then exposed to A. 'homaria.' However, this may be a feature of laboratory exposure because the field situation is much more complex with more than 100 bacterial members on the cuticle of lobsters from the affected region. However, A. 'homaria" is chitinolytic and could possibly be one of the first colonizers of lesions that are induced by environmental factors in the wild. In addition, Quinn et al. (2012) investigated a prelesion state whereas we are investigating an advanced state of disease with full-blown lesions. The observation that Jannaschia spp. and Aquimarina spp. were correlated with the disease state, albeit very weakly, suggests that these 2 species could be involved in a consortium of first colonizers of carapace lesions. However, other genera, such as Hirschia, Oceanicolaa, and so forth should be considered as possible contributors to the disease (see Table 3).
The role ofAquimarina spp. in the disease lesions is unclear. In the DA that compared the bacteria on cuticle from diseased animals with healthy cuticle from diseased animals, Aquimarina spp. had a smaller structure coefficient (0.156) than that on diseased versus apparently healthy animals. One possible interpretation is that if A. 'homaria' was present at the initiation of the disease, then it should be more strongly negatively correlated with the diseased cuticle. Instead, it correlates weakly, and positively, indicating that it discriminates the diseased state from the regions of the carapace that are not affected by the disease directly. Moreover, in the analysis of flora from healthy lobsters versus healthy cuticle on diseased lobsters, the structure coefficient of Aquimarina spp. was negative, and was the lowest value of all the structure coefficients in the structure coefficient table. This indicates a weakly negative correlation; one would expect to find slightly more of this bacterial genus on lesion-free areas of infected lobsters than would be found on lobsters that present no signs of the disease. In addition, the phylogenetic tree indicates that there may be at least 3 species/strains in the genus Aquimarina present in our lobster samples, and the major clades do not cluster with known species and are not correlated directly with the disease state. An alternative hypothesis is that A. 'homaria' could initiate the disease lesion, hut may be excluded from the mature lesion as other taxa colonize the area.
The Unifrac neighbor joining tree constructed of the microbiome data from each lobster in this study demonstrates that although there is some visible clustering of these samples, there is no clear separation of the clades based on disease class. This is also reflected in the PCO plot, in which we see some clustering of the samples based on disease class, but these clusters overlap significantly. This observation could be a result of the fact that all the lobsters were taken in a relatively small geographical region of Narragansett Bay, and presumably exposed to similar environmental factors. The DA indicated that there are 58 genera that are potentially correlated with the disease class, and no single taxa makes a substantial contribution to the metric. Furthermore, most of these taxa are present to some degree in all the sample classes.
In summary, we observed that, of 170 taxa of bacteria, 58 were different in the diseased state, yet none were found to correlate significantly with disease state. Thus, there is no clear correlation of any one taxa with the lesions, indicating that the disease may involve a dysbiotic shift in the shell microbial community resulting from some environmental factor that stresses the lobsters, thereby allowing opportunistic invasion of the carapace by bacteria that normally exist in the carapace biofilm. Although the disease manifests as a bacterial erosion of the cuticle, there is also evidence that the cuticle can be compromised structurally by alkyl phenols that interferes with cross-linking of tyrosine moieties in the premolt cuticle (Laufer et al. 2012). Kunkel et al. (2012) found that lobsters with ESD showed signs of erosion or malformation of calcite and apatite structures within the exoskeleton. Both the erosion of calcite and interference of tyrosine cross-linking by alkyl phenols may play a role in making the lobsters more susceptible to ESD. Other factors, such as pollution, increased bottom temperatures, and genetic variation in the lobsters themselves, may also contribute to morbidity.
This work was supported by the National Marine Fisheries Service as the New England Lobster Research Initiative: Lobster Shell Disease under NOAA grant NA06NMF4720100 to the University of Rhode Island Fisheries Center. We thank Dr. Kathy Castro and Barbara Somers of the University of Rhode Island for their dedication and expertise in leading the Lobster Shell Disease Initiative, and thank Dr. J. Moss and K. Wheeler, who did the lobster dissections. The views expressed herein are those of the authors and do not necessarily reflect the views of NOAA or any of its subagencies. The U.S. government is authorized to produce and distribute reprints for government purposes, notwithstanding any copyright notation that may appear hereon.
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* Corresponding author. E-mail: firstname.lastname@example.org
NORMAN J. MERES (1)* CYRIL C. AJUZIE, (3) MASOUMEH SIKAROODI, (1) MEGHANA VEMULAPALLI, (2) JEFFREY D. SHIELDS (4) AND PATRICK M. GILLEVET (1)
(1) Environmental Science and Policy Department, George Mason University, 4400 University Drive, Fairfax, VA, 22030; (2) School of Systems Biology, George Mason University, 4400 University Drive, Fairfax, VA, 22030; (3) Aquaculture, Freshwater and Marine Ecology Research Lab, Applied Fisheries and Hydrobiology Unit, Department of Zoology University of Jos, Nigeria, (4) 4Virginia Institute of Marine Science, PO Box 1346, Gloucester Point, VA, 23062
TABLE 1. Classification results table (confusion matrix) for the bacterial flora on the cuticle of diseased lobsters compared with that of apparently healthy lobsters. Classification Results * : Diseased Versus Healthy Predicted Group Membership Disease Class 1 2 Total Original Count Diseased 52 3 55 % Healthy 3 44 47 Diseased 94.5 5.5 100.0 Healthy 6.4 93.6 100.0 * A total of 94.1 % of original grouped cases correctly classified. Actual group membership is compared with that predicted by the discriminant function derived from the analysis. The analysis excludes those bacteria that did not pass the tolerance test. TABLE 2. Structure coefficient table showing the coefficients that measure the relationship with flora on the cuticle of diseased versus apparently healthy lobsters, diseased cuticle versus healthy cuticle on diseased lobsters, and healthy cuticle versus healthy cuticle on diseased lobsters. Diseased Versus Taxon Coefficient Jannaschia 0.325 Aquimarina 0.268 Hirschia 0.173 Oceanicolaa 0.152 Methylosarcinaa 0.148 Schineriaa 0.148 Shigellaa 0.143 Tenacibaculuma 0.142 Terasakiellaa 0.141 Filomicrobium 0.139 Thalassobactera 0.125 Microbulbifera 0.105 Photobacteriuma 0.104 Lacinutrix 0.099 Streptococcusa 0.092 Woodsholeaa 0.089 Vibrioa 0.073 Haliscomenobacter 0.065 Ruegeriaa 0.063 Psychrobactera 0.06 Roseovariusa 0.058 Shinellaa 0.058 Fluoribacter 0.058 Ralstoniaa 0.058 Carnobacteriaceae_2a 0.058 Curvibactera 0.058 Schlegelellaa 0.058 Diaphorobactera 0.058 Pseudomonasa 0.058 Asticcacaulisa 0.058 Stenotrophomonasa 0.058 Staphylococcusa 0.058 Colwellia 0.058 Methylobacteriuma 0.058 Hyphomicrobium 0.058 Achromatium 0.058 Geopsychrobacter 0.058 Brumimicrobium 0.058 Spirochaetaa 0.058 Caldilineacea 0.058 Agrobacterium 0.058 Cellulophaga 0.058 Anaerococcus 0.058 Roseobactera 0.049 Nereidaa -0.263 Glycomycineae -0.236 Silicibactera 0.206 Stappiaa 0.179 Cycloclasticus -0.177 Sulfitobactera -0.175 Phaeobactera -0.173 Cloacibacteriuma -0.172 Erythromicrobium -0.165 Salinibactera -0.153 Carnobacteriaceae_1 -0.15 Branhamella -0.143 Turicibacter -0.141 Leadbetterellaa -0.132 Geothermobacter -0.124 Alishewane -0.12 Hyphomonasa -0.115 Nannocystaceaea -0.113 Comamonasa -0.11 Algibacter -0.11 Piscirickettsiaa -0.11 Fluviicola -0.101 Propionibacterineaea -0.099 Chrysiogenes -0.099 Gp4a -0.096 Crenothrix -0.095 Leucothrix -0.095 Methylocapsaa -0.094 Krokinobactera -0.092 Frateuria -0.09 Zobelliaa -0.088 Sphingomonasa -0.088 Rubellimicrobiuma -0.086 Kaistia -0.083 Maribactera -0.081 Gaetbulibacter -0.078 Hoeflea -0.068 Hydrogenovibrio -0.068 Acidimicrobineae -0.068 Saccharophagusa -0.068 Meganemaa -0.068 Aminomonas -0.068 Nitrospiraa -0.068 Thiorhodospiraa -0.068 Thioclavaa -0.068 Rhodomicrobiuma -0.068 Sphingopyxisa -0.068 Pibocellaa -0.068 Winogradskyellaa -0.068 Rhodobacaa -0.063 Nitrosospiraa -0.063 Microvirgaa -0.06 Erythrobacter -0.06 Corynebacterineae -0.059 Frankineae -0.051 Micrococcineaea -0.05 Diseased Versus Healthy Diseased Taxon Coefficient Jannaschia 0.315 Aquimarina 0.156 Cycloclasticus 0.125 Hirschia 0.112 Corynebacterineae 0.076 Filomicrobium 0.073 Cardiobacterium 0.066 Leisingera 0.059 Leucothrix 0.055 Haliscomenobacter 0.051 Kaistia 0.043 Glycomycineae 0.043 Burkholderia 0.043 Frankineae 0.04 Brumimicrobium 0.03 Geopsychrobacter 0.03 Agrobacterium 0.03 Fluviicola 0.03 Achromatium 0.03 Caldifneacea 0.03 Flexithrix 0.03 Alkalilimnicola 0.03 Chromatiuma 0.03 Colwelliaa 0.03 Anaerococcus 0.03 Hoefleaa 0.03 Delftia 0.025 Turicibacter 0.031 Hyphomicrobium 0.025 Ahrensia 0.023 Lactococcus 0.021 Lacinutrix 0.019 Crenothrix 0.015 Dokdonia 0.001 Achromobacter -0.274 Cellulophaga -0.191 Chrysiogenes -0.131 Abiotrophia -0.116 Fluoribacter -0.115 Erythrobacter -0.109 Algibacter -0.091 Hydrogenovibrioa -0.079 Carnobacteriaceae-1 -0.068 Acidimicrobineae -0.052 Erythromicrobium -0.052 Geothermobacter -0.046 Oceanibulbus -0.042 Kordiimonas -0.037 Gaetbulibacter -0.015 Devosia -0.013 Pasteuriaceae_Incertae_Sedis -0.013 Kangiella -0.004 Healthy Versus Healthy on Diseased Taxon Coefficient Jannaschia 0.248 Leisingera 0.127 Corynebacterineae 0.113 Frankineae 0.112 Leucothrix 0.094 Cycloclasticus 0.09 Hirschia 0.063 Kaistia 0.062 Aminomonas 0.057 Crenothrix 0.057 Hoeflea 0.054 Erythrobacter 0.052 Cardiobacterium 0.048 Glycomycineae 0.045 Hydrogenovibrio 0.045 Frateuria 0.044 Fluviicola 0.044 Haliscomenobacter 0.044 Lactococcus 0.036 Chrysiogenes 0.035 Chromatiuma 0.031 Branhamella 0.031 Alkalilimnicola 0.031 Flexithrix 0.031 Alishewane 0.031 Burkholderia 0.031 Carnobacteriaceae-1 0.019 Ahrensia 0.013 Acidimicrobineae 0.011 Delftia 0.007 Algibacter 0.004 Aquimarina -0.002 Abiotrophia -0.004 Dokdonia -0.016 Achromobacter -0.018 Cellulophaga -0.046 Hyphomicrobium -0.046 Lacinutrix -0.046 Erythromicrobium -0.063 Fluoribacter -0.066 Gaetbulibacter -0.069 Geothermobacter -0.072 Oceanibulbus -0.09 Kordiimonas -0.103 Devosia -0.118 Pasteuriaceae_Incertae-Sedis -0.169 Kangiella -0.258 A large absolute value of a structure coefficient indicates a close relationship between the coefficient and the function. The sign indicates whether it is correlated positively or negatively to the first disease state (i.e., diseased versus healthy). TABLE 3. Classification results table for the bacterial flora on the cuticle of diseased lobsters compared with healthy cuticle on diseased lobsters. Predicted Group Membership Healthy on Disease Class Diseased Diseased Total Original Count Diseased 54 1 55 Healthy on diseased 0 32 32 % Diseased 98.2 1.8 100.0 Healthy on diseased 0.0 100.0 100.0 Actual membership is predicted by the discriminant function. The analysis discriminated 98.9% of original grouped cases correctly. TABLE 4. Classification results table for the bacterial flora on the cuticle of apparently healthy lobsters compared with that on healthy regions of diseased lobsters. Predicted Group Membership Healthy on Disease Class Healthy Diseased Total Original Count Healthy 47 0 47 Healthy on diseased 0 30 32 % Healthy 100.0 0.0 100.0 Healthy on diseased 0.0 100.0 100.0 Actual group membership is compared with that predicted by the discriminant function derived from the analysis. The analysis correctly classified 100% of the group membership.
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|Author:||Meres, Norman J.; Ajuzie, Cyril C.; Sikaroodi, Masoumeh; Vemulapalli, Meghana; Shields, Jeffrey D.;|
|Publication:||Journal of Shellfish Research|
|Date:||Jun 1, 2012|
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