Comparative genomic and phenomic analysis of Clostridium difficile and Clostridium sordellii, two related pathogens with differing host tissue preference
- Joy Scaria†1, 2,
- Haruo Suzuki†1, 3,
- Christopher P. Ptak†1,
- Jenn-Wei Chen1,
- Yongzhang Zhu1, 4,
- Xiao-Kui Guo4 and
- Yung-Fu Chang1Email author
© Scaria et al. 2015
Received: 17 December 2014
Accepted: 29 May 2015
Published: 10 June 2015
Clostridium difficile and C. sordellii are two anaerobic, spore forming, gram positive pathogens with a broad host range and the ability to cause lethal infections. Despite strong similarities between the two Clostridial strains, differences in their host tissue preference place C. difficile infections in the gastrointestinal tract and C. sordellii infections in soft tissues.
In this study, to improve our understanding of C. sordellii and C. difficile virulence and pathogenesis, we have performed a comparative genomic and phenomic analysis of the two. The global phenomes of C. difficile and C. sordellii were compared using Biolog Phenotype microarrays. When compared to C. difficile, C. sordellii was found to better utilize more complex sources of carbon and nitrogen, including peptides. Phenotype microarray comparison also revealed that C. sordellii was better able to grow in acidic pH conditions. Using next generation sequencing technology, we determined the draft genome of C. sordellii strain 8483 and performed comparative genome analysis with C. difficile and other Clostridial genomes. Comparative genome analysis revealed the presence of several enzymes, including the urease gene cluster, specific to the C. sordellii genome that confer the ability of expanded peptide utilization and survival in acidic pH.
The identified phenotypes of C. sordellii might be important in causing wound and vaginal infections respectively. Proteins involved in the metabolic differences between C. sordellii and C. difficile should be targets for further studies aimed at understanding C. difficile and C. sordellii infection site specificity and pathogenesis.
KeywordsClostridium difficile Clostridium sordellii Comparative genomics Phenotype microarray Urease
The bacterial class, Clostridia, is typified by gram-positive anaerobes and includes several important human pathogens. The main virulence factors produced by pathogenic Clostridia are secreted toxins. While C. botulinum (botulism) and C. tetani (tetanus) are the best known of these pathogens, other members, in particular C. difficile, have become increasingly notorious due to an accelerating number of documented infections in recent years. In North America and Europe, C. difficile infection (CDI) is now the leading cause of infectious diarrhea [1–3]. CDI can cause a varying range of diseases from mild diarrhea to fulminant colitis and death [4–6]. The primary risk factors of CDI include antibiotic treatment, advanced age, severe underlying illness, prior hospitalization, tube feeding, gastrointestinal surgery, and use of proton-pump inhibitors [7, 8]. C. difficile also has a broad host range and causes infection in agriculturally important animals such as pigs, cattle, horses and chickens [9–13].
Recent studies on the Clostridium genus support a reclassification of C. difficile and the related Cluster XI into a family-level group that is distinct from the current Clostridiaceae family (renaming family genus spp. to Peptostreptococcaceae Peptoclostridium difficile has been suggested) . Along with C. difficile, Cluster XI currently includes several clinically significant members, C. sordellii, Filifactor alocis, and Peptostreptococcus anaerobius [15–17]. C. sordellii infection (CSI), although not as prevalent as CDI, has a very high mortality rate that can often reach 75 % lethality . Both C. sordellii and C. difficile are asymptomatically carried in the gastrointestinal tracts of about 10 % of adult humans [15, 18] and both species also can infect animals [19–22]. In addition, C. sordellii and C. difficile excrete potent toxins with immunological cross-reactivity and similar biological activities [15, 23, 24]. Despite the close similarities in host range and virulence factors, there are two striking differences between C. difficile and C. sordellii. First is that while C. difficile only colonizes the gastrointestinal tract, C. sordellii can colonize both the human gastrointestinal tract and vagina . Secondly, while C. difficile infection affects the host intestine, C. sordellii primarily causes soft tissue infection. The tissue preference of C. sordellii results in CSI being primarily reported among reproductive-age women following natural childbirth, spontaneous, surgical or medical abortions [15, 26]. Wounds from illicit injectable drug use, non-gynecological surgical procedures, penetrating crushing injuries, or traumatic injury in previously healthy men, women, and children can also lead to CSI infection [27–32].
Comparative analyses of closely related bacteria with different infection site specificity and pathogenicity can provide information relevant to understanding adaptation to host environments and mechanisms of infection. Genomic differences can lead to phenotype level changes. In bacteria, phenotypic variations are often related to metabolic changes, which are defined by the ability to utilize various sources of carbon, nitrogen, sulfur, phosphorous, and other essential nutrients. With the development of Phenotype microarrays (PMs), high-throughput determination of a microorganism’s global metabolic phenotype or phenome is now possible [33–37]. In this study, to determine the genomic and phenomic basis for the differences in C. sordellii and C. difficile infections, we have performed a comparative genomic and phenomic analysis of these two species. The global phenome of C. sordellii and C. difficile revealed several differences, most notably in acid resistant growth. We further explored the genomic basis for phenomic differences by determining a draft genome sequence of C. sordellii strain 8483 and comparing it against two C. sordellii genomes (strains ATCC_9714, and VPI_9048) and eight C. difficile genomes (strains 630, BI1, CD196, M68, R20291, 2007855, CF5, and M120). Understanding the differential adaptions to host tissue at the genomic and phenomic level should provide opportunities in the fight against these important infections.
Results and discussion
Nutritional phenomic comparison
In this study, we have performed the comparative analysis of the genome and phenome of the two closely related Clostridial pathogens, C. sordellii and C. difficile. To determine the global phenome of C. sordellii and C. difficile, we used Biolog phenotype microarrays (PMs) which enable whole cellular level determination of bacterial phenotypes . The nutritional PM analysis consisted of 190 assays of carbon source metabolism, 94 assays of phosphorous and sulfur source metabolism, 95 assays of biosynthetic pathways, and 380 assays of nitrogen source metabolism . We have previously analyzed the phenome of six C. difficile strains . We compared those results with phenome of C. sordellii strain 8483 determined in this study. From a total of 759 nutritional phenotype assays, 160 were positive (indicated by a 40 % growth enhancement relative to the control) for C. sordellii strain 8483, while 132 were positive for C. difficile strain 630 (Additional file 1: Table S1 and Additional file 2: Table S2). C. sordellii and C. difficile shared 65 positive phenotypes while 162 positive phenotypes were specific to just one of the two species. A disproportionate number of unshared phenotypes were identified in the carbon and nitrogen source assays (~3.0× unshared vs. shared for carbon and nitrogen combined; ~1.3× unshared vs. shared for other sources combined). The unshared carbon and nitrogen phenotypes are indicative of species specific adaptations to environmentally available nutrient sources.
Our analysis finds that, at the phenome level, both of these closely related species share a core group of functions and phenotypes. Our phenotype array results also reveal some key phenotype differences particularly for carbon and nitrogen source utilization that might explain the differences in the primary site of infections caused by these two species. Both glycolysis and amino acid catabolism are differentially regulated in C. difficile suggesting a possible mechanism for how these pathways have evolved to better occupy the respective niche for both C. sordellii and C. difficile .
Osmolyte and pH phenomic comparison
C. difficile showed better adaptation to growth under 78 % of osmolyte conditions; however, C. sordellii had a growth advantage over C. difficile in 83 % of urea-specific conditions. The combination of low pH and urea led to a > 40 % growth enhancement for C. sordellii over pH 6.0 conditions while the same combination inhibited growth in C. difficile by > 40 % relative to optimal growth at pH 6.0. C. sordellii has previously been shown to exhibit urease activity . Details on the C. sordellii urease gene cluster and its implications for growth improvement under multiple conditions including acidic pH are discussed in the later sections.
Species with similar gene contents should have similar functional potential as a whole . Whole gene contents of any genome can be the result of a mixture of different evolutionary events such as vertical inheritance of genes and their duplication, gain and loss events, and their relative contributions may vary among different species . The 22,612 proteins from the 9 bacterial species from Clostridial clusters I, XI, XII, and XIII were classified into 5979 homologous groups or protein families. Of the 5979 protein families, 3578 were present in a single strain, 2401 were present in two or more strains, of which 487 were shared by all the strains.
Gene repertoire comparison between C. sordellii and C. difficile
We compared the gene repertoire between eight C. difficile strains (630, BI1, CD196, M68, R20291, 2007855, CF5, and M120) and three C. sordellii strains (8483, ATCC_9714, and VPI_9048). Proteins from the 11 strains were classified into 4368 homologous groups (protein families); (see Additional file 4: Table S4 for a comprehensive list). Of the 4368 protein families, 928 were present in a single strain, 3440 were present in two or more strains, of which 1395 were shared by all the strains.
A total of 738 protein families were present in all the 3 C. sordellii strains but absent in all the 8 C. difficile strains (Additional file 4: Table S4). These C. sordellii-specific genes may have been gained on the branch leading to the C. sordellii strain, and could be linked to its specific environmental adaptation and pathogenesis. They included some genes in the pathogenicity locus; i.e., locus_tag WS9_01807 to WS9_01812 in strain 8483, H476_0268 to H476_0289 in strain VPI_9048, and H477_0262 to H477_0286 for strain ATCC_9714 . They included several amino acid decarboxylases and deaminases (e.g., glutamate decarboxylase [EC:220.127.116.11], histidine decarboxylase [EC:18.104.22.168], and L-serine deaminase) that might enable C. sordellii to grow on peptide nutrient sources in soft tissue. In addition, glutamate decarboxylase and arginine deiminase [EC 22.214.171.124] produce alkaline byproducts that have been suggested to participate in the acid resistance of gram-positive bacteria  and improve C. sordellii growth in acidic environments. Two protein families annotated as “KUP system potassium uptake protein (K03549)” and “potassium voltage-gated channel Shab-related subfamily B member 1 (K04885)” were present in C. sordellii strains but absent in C. difficile strains. Proteins involved in potassium transport often play a role in adaptive pH tolerance  and may increase broad pH range survival in C. sordellii.
The SEED annotation engine defines genes associated with a functional role in a bacterial genome as a subsystem . A SEED subsystem is termed as a generalization of the term “pathway” and is a convenient framework for functional comparisons of bacterial genomes . Of the 4368 protein families, 854 were assigned to the SEED subsystems (Additional file 4: Table S4). Both C. sordellii and C. difficile contained some of the important functions relevant to strain transmission and colonization. For example, both species contained 20 protein families assigned to “Dormancy and Sporulation” including sporulation sigma factor. Genes related to spore coat (cotA, cotB, cotCB, cotD, and cotE) are present in both C. difficile and C. sordellii genomes. This is consistent with the previous reports on C. difficile and C. sordellii spore properties [25, 56, 57]. Of the 14 protein families assigned to “Iron acquisition and metabolism”, hemerythrin-like iron-binding protein was present in C. sordellii but absent in C. difficile. Iron acquisition is essential for growth of pathogenic bacteria during soft tissue infections  and is likely to be important for C. sordellii in proliferating in host tissues. It has been shown that Stickland metabolism is important in C. difficile physiology . In C. difficile, several genes located in D-proline reductase operon and glycine reductase operon are involved in Stickland associated metabolism. When C. sordellii genomes were compared to C. difficile genomes, most genes in the prd (prdC, prdR, prdA, prdB, prdD, prdE) and grd (grdA, grdB, grdC, grdD, grdE, and grdX) operons were found to be conserved.
Virulence Factors Database (VFDB)
We used the Virulence Factors Database (VFDB)  to assess the presence of virulence genes in the C. sordellii and C. difficile strains (Additional file 4: Table S4). Toxins A (tcdA) and B (tcdB) were homologous, and the homologous proteins were present in all the 11 genomes of C. difficile and C. sordellii. For strain ATCC_9714, locus_tag H477_0265 (Truncated TcsH) is annotated as toxin A, and H477_0263 (Cytotoxin L) is annotated as “toxin B”. For VPI_9048, locus_tag H476_0269 [cytotoxin L (TcsL)] and H476_0271 [Hemorrhagic toxin (TcsH)] are annotated as toxin B . In the strain C. sordellii 8483 locus tags WS9_01807 to WS9_01812 corresponds to toxin B and locus tag WS9_01787 correspond to toxin A. However, C. sordellii being a draft genomes, the locus tags in C. sordellii strains VPI_9048 and 8483 represent partial sequence of the toxin genes.
Four protein families homologous to collagenase (colA), sialidase (nanH), perfringolysin O (pfoA), and phospholipase C (plc), respectively, from C. perfringens were present in C. sordellii but absent in C. difficile. At the genome level, C. sordellii contains genes encoding enzymes for host tissue lysis and nutrient release during infection (e.g., hyaluronidase and hemolysin) . The presence of these enzymes coupled with the ability to metabolize a larger set of peptides is likely to be a contributing factor in the ability of C. sordellii to cause lethal soft tissue infections. In addition, a cluster of eight genes encoding urease subunits (UreA (λ), UreB (β) and UreC (α)) and urease accessory proteins (UreI, UreE, UreF, UreG and UreH) homologous to known virulence factors of Helicobacter pylori 26695 (Enzyme; Acid resistance; Colonization) were present in C. sordellii but absent in C. difficile.
Clusters of Orthologous Groups (COG)
Protein families assigned to COG functional category E (Amino acid transport and metabolism) that are present in C. sordellii strain 8483 but absent in C. difficile strain 630
COG functional annotation
COG0076E|Glutamate decarboxylase and related PLP-dependent proteins
COG0115EH|Branched-chain amino acid aminotransferase/4-amino-4-deoxychorismate lyase
COG0346E|Lactoylglutathione lyase and related lyases
COG0477GEPR|Permeases of the major facilitator superfamily
COG0493ER|NADPH-dependent glutamate synthase beta chain and related oxidoreductases
COG0697GER|Permeases of the drug/metabolite transporter (DMT) superfamily
COG0747E|ABC-type dipeptide transport system, periplasmic component
COG0757E|3-dehydroquinate dehydratase II
COG0804E|Urea amidohydrolase (urease) alpha subunit
COG0831E|Urea amidohydrolase (urease) gamma subunit
COG0832E|Urea amidohydrolase (urease) beta subunit
COG0834ET|ABC-type amino acid transport/signal transduction systems, periplasmic component/domain
COG1104E|Cysteine sulfinate desulfinase/cysteine desulfurase and related enzymes
COG1410E|Methionine synthase I, cobalamin-binding domain
COG1703E|Putative periplasmic protein kinase ArgK and related GTPases of G3E family
COG2755E|Lysophospholipase L1 and related esterases
COG2856E|Predicted Zn peptidase
COG3227E|Zinc metalloprotease (elastase)
COG4608E|ABC-type oligopeptide transport system, ATPase component
Urease gene cluster in environmental adaptation
Other genes involved in environmental stress tolerance
Several other C. sordellii genes that are absent from C. difficile could play a role in adaptation to acidic conditions (Table 1; Fig. 9a). Glutamate decarboxylase converts glutamate to γ-aminobutyric acid (GABA) which absorbs a proton during the reaction. Arginine deiminase generates ammonia which can also absorb a proton and create a more alkaline internal, periplasmic, or local pH in an acidic environment. Some bacteria can accumulate high cytoplasmic potassium levels under acidic stress. Potassium transport is expected to play a role in adaptation to acidic environments by maintaining the membrane potential for optimum bioenergetics homeostasis [68, 69], yet the exact mechanism of how the various transporters and channels work together to support the internal pH is not fully understood. Potassium homeostasis is also pivotal in the osmotic stress response.
Similar growth of C. sordellii strain 8483 from pH 6 to a more alkaline pH range was observed (Fig. 3). A homologue of the Na+/H+ antiporter, NhaA, was identified in the genome of C. sordellii strains 8483, ATCC 9714 and VPI 9048 but was identified only in C. difficile strain F501. In Escherichia coli and Salmonella enterica, NhaA has been implicated as a mechanism for maintaining internal pH homeostasis under alkaline conditions by catalyzing H+ uptake for a preferred pHout range of 6.5 to 8.5 [70, 71]. The presence of NhaA in all C. sordellii strains but absence from most C. difficile strains could explain the ability of C. sordellii to maintain growth levels at high pH (Fig. 9b). Finally, some of the observed phenotypic differences could be multifactorial and related to differences in the gene expression levels of many genes. As is the case with the activation of toxin genes in C. difficile, gene expression levels are likely to be correlated to several components in the bacterium's nutritional environment, such as the presence of sugars, amino acids, and fatty acids [72–74].
The related pathogens, C. difficile and C. sordellii, were compared through the analysis of phenomic and genomic datasets. While C. difficile infections have been well studied, significantly less information regarding C. sordellii infections is available. In particular, the current study focused on uncovering the basis for C. sordellii‘s preference for infecting both soft tissues and the vagina, while not infecting the gastrointestinal tract (a major clinical difference between C. difficile and C. sordellii). A comparison of the phenome between C. difficile and C. sordellii revealed that C. sordellii had adapted to survive under conditions that require the procurement of resources from host tissue. In addition, C. sordellii can withstand more acidic pH than C. difficile thereby allowing it to survive in the low pH environment of the vagina. The complementary genomic analysis revealed a large number of proteins present in C. sordellii but not in C. difficile that are likely to play an adaptive role in metabolism and pH tolerance. In this context, the urease gene cluster is described in detail. The phenomic and genomic comparison between C. difficile and C. sordellii should provide guidance for the development of targeted treatments for Clostridial infections.
Bacterial culturing and phenotype microarray experiments
The global nutritional phenome of C. sordellii strain 8483 was measured using Biolog Phenotype microarrays (PMs). C. sordellii strain 8483 is a human blood isolate obtained from the United Sates Centers for Disease Control and Prevention. PM Technology consists of different PM panels, of which PMs 1–8 are linked to nutrient utilization (metabolism) and PMs 9–10 are related to chemical sensitivity. We have used PMs 1–8 to analyze the global nutritional phenome of C. sordellii and C. difficile and PMs 9–10 to test osmolyte and pH sensitivities. All experiments were conducted in a Bactron IV anaerobic chamber (Shell Lab, OR). Prior to PM experiments, C. sordellii 8483 was grown in anaerobic Brain Heart Infusion (BHI) broth. PM experiments were performed following standard Biolog Inc. protocol . Briefly, 300 μl of the bacteria grown in BHI broth was plated on Biolog Universal blood agar plates and was incubated overnight at 37 °C. A 40 % transmittance cell suspension in Biolog solution IF-0a was then prepared by re-suspending bacteria grown on Biolog Universal blood agar plates. This suspension was then diluted with Biolog mix B at a ratio of 1:16 and then transferred to each of the 96-well PM microplates (a set of 95 substrates and one blank well). The inoculation volume was 100 μl/well. The plates were incubated at 37 °C for 48 h. The optical density (OD) values were then measured at 750 nm using an ELISA reader. Each experiment was performed as biological triplicates.
For statistical analysis of the PM data, means of the replicates were taken. For normalizing the data between strains, each PM well’s mean was divided with the mean of the respective plates negative control. The value of each well was then compared using ANOVA to the negative control value of the respective plate. A PM well was considered positive if its value was 40 % higher than the negative control at 5.0 % significance level. The Model SEED database  was then used to predict the genome scale metabolic phenotype of C. sordellii and C. difficile. For phenotype comparisons of C. difficile with C. sordellii, sum of positive phenotypes of C. difficile strains  was taken. We compared the predicted metabolic phenotypes with the positive PM results. This comparison showed that at 40 % growth increase cut off from negative control, false positives are completely avoided.
Genomic DNA isolation, genome sequencing and data collation
For isolating genomic DNA, C. sordellii strain 8483 was streaked on BHI agar plate and incubated anaerobically at 37 °C overnight. A single colony from this plate was then used to inoculate BHI broth and was incubated at anaerobic conditions for 12 h. From 1.0 ml of this culture, following manufacturer’s protocol, genomic DNA was isolated using MasterPure™ Gram Positive DNA Purification Kit (Epicenter Biotechnologies, Madison, WI). Roche/454 pyrosequencing, involving paired-end reads from the FLX sequencer, was used to determine the genome sequence of C. sordellii strain 8483 with sequencing coverage of 35x. The sequences were assembled De novo using Newbler Software Release: 2.5.3. Genome annotation for the strain was done by the National Center for Biotechnology Information (NCBI) Prokaryotic Genomes Automatic Annotation Pipeline. The C. sordellii whole genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession AJXR00000000. The version described in this paper is the first version, AJXR01000000.
We used Bioperl version 1.6.1  and G-language Genome Analysis Environment version 1.8.13 (http://www.g-language.org) [77–79] for sequence data analysis, and R version 3.1.0 for statistical computing (http://www.R-project.org) . For comparative analysis, bacterial genome sequences in GenBank format  were retrieved from the NCBI FTP site (ftp://ftp.ncbi.nih.gov/) and from the PATRIC  FTP site (ftp://ftp.patricbrc.org/patric2/genomes/). Protein-coding sequences were retrieved from the bacterial genomes. Homologous proteins were identified by the BLAST (Basic Local Alignment Search Tool) program  with an E value cutoff of 1e-5 and a minimum aligned sequence length coverage of 50 % of a query sequence.
A group of orthologous proteins was built by all-against-all protein sequence comparison using BLASTP followed by FastOrtho with default parameters (http://enews.patricbrc.org/fastortho/), which is a reimplementation of the OrthoMCL program . We used the 351 ortholog groups shared by all the strains and contained only a single copy from each strain. These orthologs were aligned as follows: i.e., nucleotide sequences are translated into amino acid sequences, aligned with MUSCLE [85, 86], back translated into nucleotide sequences, and ambiguous regions (containing gaps and poorly aligned) were eliminated with Gblocks [87, 88]. The orthologs with more than 50 % of their regions removed are disregarded from the phylogenetic analysis. This retained 346 reliably aligned orthologs from a set of the 351 orthologs. A phylogenetic tree for each of the 346 orthologous genes (gene tree) was reconstructed using RAxML  with the GTRGAMMA model. A majority-rule consensus (extended) of the gene trees was constructed using consense program of PHYLIP 3.69 . Because the selection of genes with stronger phylogenetic signal reduced incongruence , we analyzed the data set of comprising genes whose bootstrap consensus trees showed average bootstrap support across all internodes that was greater than or equal to 90 % (134 genes). The alignments from the set of the 134 orthologous genes were concatenated, and a tree search was performed using RAxML with the same settings as for the individual gene trees. Phylogenetic trees were drawn using DendroPy  and the R package APE (Analysis of Phylogenetics and Evolution) .
Gene repertoire analysis
A group of homologous proteins (protein family) was built by all-against-all protein sequence comparison using BLASTP followed by Markov clustering (MCL)  with an inflation factor of 1.2 using MCLBLASTLINE (http://micans.org/mcl/). To detect missed protein-coding sequences due to differences in gene finding algorithms , we performed TBLASTN homology searches of each strain’s proteins against the other strain’s whole nucleotide sequence. The resulting gene content (binary data, 1 or 0, representing presence or absence of each protein family) is shown in Additional file 4: Table S4.
We used Jaccard distance (one minus Jaccard coefficient) to measure a distance between two genomes based on binary data, 1 or 0, representing the presence or absence of each protein family for each genome (gene content). The resulting distance matrix was subject to a neighbour-joining clustering and hierarchical clustering with three agglomeration methods (i.e., single-, complete-, and average-linkage clustering), and dendrograms were drawn to visualize the clustering results.
Gene functional annotation
We assigned functional annotations to each protein family by merging all the functional annotations of proteins belonging to the same family. To gain different aspects and maximize coverage, protein families were annotated by multiple databases. We performed BLASTP searches of protein sequences against NCBI nr (non-redundant) database, COG , KEGG , UniProtKB/Uniref90 , Virulence Factors Database (VFDB) , and assigned the functional annotations of the most similar protein sequences in each database. We converted protein_ID to subsystems (Category, Subcategory, Subsystem, and Role) in SEED database . We also searched protein sequences against the Pfam library of hidden Markov models (HMMs)  using HMMER, and mapped Gene Ontology (GO) terms to Pfam entries using the ‘pfam2go’ mapping provided by the GO consortium .
Availability of supporting Data
The data sets supporting the results of this article are included within the article and its additional files. The genome sequence data for the C. sordellii whole genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession AJXR00000000. The version described in this paper is the first version, AJXR01000000. The phylogenetic trees described in this manuscript have been deposited to TreeBase. Access to the data is available upon publication at http://purl.org/phylo/treebase/phylows/study/TB2:S17636.
Brain Heart Infusion
C. difficile infectio
C. sordellii infection
hidden Markov models
- plc :
- nanH :
Virulence Factors Database
This work was partially supported with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services under contract, N01-AI-30054, Project No. ZC005-06 and ZC008-09 and a grant (NYCV-478820) from the USDA Animal Health and Disease Research Program.
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