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A Nonsynonymous/Synonymous Substitution Analysis of the B56 Gene Family Aids in Understanding B56 Isoform Diversity

Gene duplication leads to the formation of gene families, wherein purifying or neutral selection maintains the original gene function, while diversifying selection confers new functions onto duplicated genes. The B56 gene family is highly conserved; it is encoded by one gene in protists and fungi, and five genes in vertebrates. B56 regulates protein phosphatase 2A (PP2A), an abundant heterotrimeric serine/threonine phosphatase that functions as a tumor suppressor and consists of a scaffolding “A” and catalytic “C” subunit heterodimer bound to a regulatory “B” subunit. Individual regulatory B56 subunits confer disparate functions onto PP2A in various cell-cell signaling pathways. B56 proteins share a conserved central core domain, but have divergent N- and C-termini which play a role in isoform specificity. We carried out a nonsynonymous/synonymous substitution analysis to better understand the divergence of vertebrate B56 genes. When five B56 paralogs from ten vertebrate species were analyzed, the gene family displayed purifying selection; stronger purifying selection was revealed when individual B56 isoforms were analyzed separately. The B56 core experienced stronger purifying selection than the N- and C-termini, which correlates with the presence of several contacts between the core and the AC heterodimer. Indeed, the majority of the contact points that we analyzed between B56 and the AC heterodimer experienced strong purifying selection. B56 subfamilies showed distinct patterns of selection in their N- and C-termini. The C-terminus of the B56-1 subfamily and the N-terminus of the B56-2 subfamily exhibited strong purifying selection, suggesting that these termini carry out subfamily-specific functions, while the opposite termini exhibited diversifying selection and likely carry out isoform-specific functions. We also found reduced synonymous substitutions at the N- and C-termini when grouping B56 genes by species but not by isoform, suggesting species-specific codon bias may have a role in regulating B56 gene expression.

Introduction
The roles of kinases in signal transduction cascades are intensely studied, but much less is known about the counter-regulatory role of protein phosphatases. The human genome encodes approximately 400 serine/threonine kinases, but only about 30 serine/threonine phosphatase catalytic subunits [1]. Because of this, phosphatases were originally thought to have wide-ranging, constitutive activities. However, rather than having numerous catalytic subunits, serine/ threonine phosphatases achieve diversity by forming distinct protein complexes. PP2A forms a heterotrimer with a scaffolding A subunit, a catalytic C subunit, and one of at least three different B regulatory subunit gene families (B55/PR55/B, B56/PR56/B', and B72/PR72/B'') [2]. Due to the combinatorial effects of the association of multiple subunits, and the inclusion of alternative splicing, PP2A may form as many as 200 different phosphatase complexes. B subunits are much more diverse than the A and C subunits, therefore they are the major contributors to PP2A substrate specificity and subcellular localization.
B56 proteins are highly conserved between species, sharing approximately 60% identity between human and yeast. The five human B56 paralogs (B56α, B56β, B56γ, B56δ, and B56ε) share 66% to 81% identity. B56 genes encode proteins with a highly conserved core of about 400 amino acids and variable N-and C-termini ranging from approximately ten to one hundred amino acids in humans. Even though B56 isoforms are highly conserved, they maintain distinct cellular functions, as their N-and C-termini are thought to provide isoform specificity. Alternative splicing occurs at the B56γ locus to produce transcripts with either a B56γ N-terminal extension (B56γ/γ) or a B56δ-like N-terminal extension (B56δ/γ) [3]. B56γ/γ and B56δ/γ are likely to have distinct roles in the cell, since their N-termini originate from two different B56 paralogs.
B56 carries out essential functions in numerous cell-cell signaling pathways through its role as a regulatory subunit of PP2A, and by and large these activities are isoform specific. For example, B56 isoforms modulate canonical Wnt signaling; most B56 isoforms inhibit, whereas B56ε is required for, Wnt signaling [4][5][6]. Casein kinase Iδ and Iε, both activators of Wnt signaling, dissociate PP2A from a protein complex that inhibits Wnt signaling [7]. Although multiple B56 subunits modulate Wnt signaling, the position at which they act varies. B56ε acts upstream of disheveled, whereas B56α acts upstream, and B56δ/γ acts downstream, of β-catenin ( [4][5][6] and J. M. Seeling, unpublished data). B56α, but none of the other isoforms, promotes the proteasomal-mediated degradation of Myc, thereby inhibiting Myc-dependent transcription [8]. In addition to activating Wnt signaling, B56ε also positively regulates Hedgehog signaling [9]. B56β and B56δ induce neural cell differentiation through the activation of nerve growth factor (NGF) signaling [10]. Loss-of-function studies showed that B56δ, but none of the other isoforms, dephosphorylates CCAAT/enhancer binding protein β (C/EBPβ) and thereby promotes adipogenesis [11]. B56β dephosphorylates and thereby inactivates Akt, a protein kinase central to multiple growth factor signaling pathways [12]. B56γ has a direct role as a tumor suppressor, as it inhibits cell spreading and metastasis by dephosphorylating paxillin, and its loss of function cooperates with telomerase, Ras, and SV40 large T antigen to transform human cells in culture [13,14]. In addition, mouse knockouts show that B56γ is required for heart development [15].
Protein-coding sequences can be exposed to neutral, purifying, or diversifying selection. Selective pressures may be dissimilar in distinct parts of a gene sequence, resulting in concurrent neutral, purifying, and/or diversifying selection at different positions within a gene. Gene duplication plays a vital role in organismal complexity and the evolution of new gene function [16]. Indeed, 15% of human genes likely resulted from gene duplication [17]. Diversifying selection plays a key role in the early stages after gene duplication, and twenty to thirty percent of the time, one gene of a duplicate gene pair experiences either neutral divergence or diversifying selection [18]. Subsequent to diversification, the majority of paralogs experience purifying selection to maintain their newly acquired function [19].
Most duplicate genes are lost, however, genes involved in signal transduction pathways and/ or development often remain [20]. In signal transduction pathways, ligands and receptors usually evolve quickly, whereas regulatory proteins such as G proteins, kinases, and protein phosphatases generally evolve more slowly [21]. For example, the Gli gene family, which encodes a transcription factor that regulates hedgehog signaling, exhibits neutral and purifying selection in mammals [22].
We previously constructed phylogenetic trees to explore the evolution of the B56 gene family in protists, plants, fungi, and animals [23]. The B56 gene was duplicated prior to the divergence of diploblasts and triploblasts (species with two or three germ layers, respectively), and likely twice more after the divergence of echinoderms from simple chordates. These duplications and subsequent gene losses gave rise to the five B56 loci currently found in vertebrates lineages, which consist of three B56-1 subfamily genes (B56α, B56β, and B56ε) and two B56-2 subfamily genes (B56γ and B56δ). At least two vertebrate lineages have lost one of the five B56 paralogs; Xenopus lacks B56δ and Aves lacks B56β [23]. Here we explored the nonsynonymous and synonymous substitution rates within the B56 gene family of PP2A regulatory subunits to gain a deeper understanding of the five B56 vertebrate genes that differentially regulate cell-cell signaling pathways.

Selection and alignment of B56 gene homologs
Species with gene sequences for all five B56 paralogs (B56α, B56β, B56γ, B56δ, and B56ε), and the B56δ/γ splice variant, were selected from NCBI on the basis of high query coverage and identity toward human B56 isoforms [24]. Each isoform, with the exception of B56δ/γ, was identified in ten vertebrate species: Danio rerio, Stegastes partitus, Alligator sinensis, Chrysemys picta bellii, Mus musculus, Rattus novregicus, Ovies aries, Bos taurus, Felis catus, and Homo sapiens. B56δ/γ sequences were identified from each of the species listed above except A. sinensis and C. p. bellii.
Nucleotide sequences were converted into the corresponding amino acid sequences using the "nt2aa" function of MATLAB 7.11.0.584 (R2010b) (MathWorks, Natick, MA). Alignments were generated from these amino acid sequences using Clustal Omega by EMBL-EBI [25]. Clustal Omega was used as an alignment tool because of its ability to align full-length sequences with large, non-conserved terminal ends [26]. These alignments were used as a guide to insert the appropriate gaps in the original nucleotide sequences that corresponded to the aligned protein sequences. The alignment of all B56 isoforms is shown in S1 Fig.

dN/dS Calculation
The nonsynonymous and synonymous substitution rates were estimated with the dN/dS function using the NG method of the Bioinformatics Toolbox in commercial software package MATLAB 7.11 (R2010B) (MathWorks, Natick, MA, USA). The NG method takes into account the number of synonymous and nonsynonymous substitutions and the number of potential synonymous and nonsynonymous sites [27]. dS substitution rates above 1.2-1.5 are considered saturated with synonymous substitutions, and may give rise to ω values which are not reliable indicators of selective pressure [28,29]. However, overall profiles did not change markedly when ω ratios with dS substitution rates above 1.5 were excluded.
As the dN/dS function eliminated all sequences at a given position if a gap was present in one or more of the aligned sequences, less of the N-and C-termini were incorporated into alignments that included a greater number of B56 paralogs. In the analysis of all of the B56 isoforms, 20 amino acids of the N-terminus were included (19% to 63% of the N-terminus, depending on the isoform). 29 amino acids of the C-terminus were included (27% to 94% of the C-terminus). With the B56-1 subfamily, 48 amino acids of the N-terminus were included, or 72% to 91% of the N-terminus, over twice that included in the family-wide analysis. With regard to the B56-2 subfamily, 30 amino acids of the N-terminus were included, or 28% to 94% of the N-terminus, a 1.5 fold increase over the family-wide analysis. 105 amino acids of the Cterminus were included, or 99 to 100% of the C-terminus, an almost four fold increase over the family-wide analysis.
To determine the selective pressure experienced by B56 subdomains, dN, dS, and ω values were calculated using a 50 codon sliding window over the entire aligned length. dN, dS, and ω values were calculated for each gene pair, after which the values for each window were divided by the number of possible pairs to get the average value for each window. The resulting averaged dN, dS, and ω values are shown at the first codon position of each 50 codon window. Due to the window size, the last 49 amino acids of the C-termini were not plotted because the dN, dS, or ω values for the last 49 amino acids of the C-termini could not be calculated. In the single amino acid analysis, undefined dN/dS ratios in which the denominator was zero did not generate a number that could be graphed. Therefore, less than four percent of the ω ratios were graphed.

Statistical Analysis
The Mann-Whitney Test was used in a pairwise test to determine whether the medians of the dN, dS, and ω values were significantly different [30]. Medians were used as the test statistic because the data had variable sample size, contained significant outliers, and were not normal (even after a log transformation). p values less than 0.05 were considered significant.

Results and Discussion
The vertebrate B56 gene family experienced purifying selective pressure To better understand how five vertebrate B56 gene copies evolved into distinct genes with different functions, we analyzed the rate of nonsynonymous (dN) and synonymous (dS) substitutions during the evolution of the B56 gene family in vertebrates. We limited our analyses to ten species which possess all five B56 genes: Danio rerio, Stegastes partitus, Alligator sinensis, Chrysemys picta bellii, Bos taurus, Ovis aries, Felis catus, Rattus norvegicus, Mus musculus, and Homo sapiens. We initially examined the entire group of fifty vertebrate B56 genes. Because the N-and C-termini were not highly conserved between B56 paralogs, sequence alignments introduced more gaps in these domains than in the core domain (S1 Fig).
B56 family and subfamilies. Each of the resultant dN/dS (ω) ratios was less than one when the entire gene was analyzed, signifying purifying selection (Fig 1 and S2 Fig). In fact, most ω ratios were less than 0.3, indicating strong purifying selection. However, one gene pair within the B56-2 subfamily, the B56γ and B56δ genes from S. partitus, exhibited close to neutral selection. This was due to the pair's low dS substitution rate, as their dN substitution rate was similar to other gene pairs in this analysis. This low dS rate suggests that synonymous changes may be selected against due to codon bias in the B56-2 subfamily in S. partitus. Synonymous substitutions have been shown to alter gene expression through several mechanisms, e.g., its effects on cis regulatory elements, mRNA secondary structure, and/or rates of translation [31].
To better understand B56 subfamily evolution, we determined ω separately for the B56-1 and B56-2 subfamilies. Both subfamilies displayed strong purifying selection, as all ω ratios were less than one (Fig 1). The B56-1 and B56-2 subfamilies experienced stronger purifying selection than the entire B56 family, as would be expected if the B56-1 and B56-2 subfamilies experienced distinct forces of purifying selection (S2 Fig, S1 and S2 Tables). The synonymous substitution rate was lower in the B56-1 subfamily than the B56-2 subfamily or when all paralogs were analyzed together (S2 Fig, S1 and S2 Tables). This suggests that synonymous changes in the B56-1 subfamily may be selected against due to codon bias.
Individual B56 isoforms. To specifically examine selection occurring within individual isoforms, each of the five vertebrate B56 isoforms was examined separately. These analyses included the B56δ/γ splice variant from each of the species listed previously except A. sinensis and C. p. bellii, where sequences of sufficient length could not be identified. Each of the isoforms displayed strong purifying selection (Fig 2). B56ε displayed an order of magnitude lower ω ratio than the other isoforms, suggesting that it experienced very strong purifying selection, or that it arose more recently (Fig 2 and S2 Fig). B56β displayed the weakest purifying selection, and the other B56 isoforms exhibited intermediate levels of purifying selection (S2 Fig). Importantly, the ω ratios from the analyses of all B56 isoforms were higher than those of individual isoforms, suggesting that the individual isoforms have undergone distinct paths of purifying selection (Figs 1 and 2, S2 Fig).
The ω plot of B56ε possessed an outlier, the S. partitus and D. rerio pairing, with an approximately five fold higher ω ratio than the other gene pairs (Fig 2). This species pair also possessed high dN and dS substitution rates for B56α, B56γ, and B56δ/γ. This may reflect reduced purifying selection of B56ε and the other isoforms in fish, due to elevated rates of substitution in fish versus mammals (and presumably other vertebrates) [32].
The N-and C-termini experienced differential selection It has long been hypothesized that the core, which consists primarily of α-helices and interconnecting loops, interacts with the A and C subunits of the heterotrimer, while the N-and C-termini bind the substrate and determine subcellular localization. As the core and the Nand C-termini are proposed to have distinct functions, it is likely that they may have experienced different selective pressures. We therefore analyzed dN and dS substitution rates for the core and the N-and C-terminal domains separately (S3 Table).
B56 family and subfamilies. When all of the B56 paralogs, the B56-1 subfamily, or the B56-2 subfamily were analyzed, the core exhibited stronger purifying selection than the whole gene, (Fig 1 and S3 Fig, S4 and S5 Tables). In addition, the core experienced stronger purifying selection in the subfamilies versus the entire gene family. The reduced ω ratios in both subfamilies as compared to the family-wide analysis suggest that significant purifying selection of the core occurred after the duplication that produced the B56-1 and B56-2 subfamily progenitors. Although the core makes contacts with the A and C subunits, portions of it are also thought to interact with substrates, and subfamily-specific substrate-binding sites may account for the reduced purifying selection in the family-wide analysis. In addition, the B56-2 core experienced stronger purifying selection than B56-1. B56-1 and B56-2 had similar dS values, so this is less likely due to the two B56-2 genes being products of a more recent duplication event than to constraints on B56-2 amino acid substitutions.
The N-and C-termini contrast with the core, showing weaker purifying selection. When all of the B56 sequences were analyzed, a significant number of gene pairs displayed ω ratios equal to or greater than one, indicating neutral or divergent selection, respectively (Fig 1). However, many ω ratios were less than one and therefore in the purifying range. The N-terminus possessed more gene pairs with strong diversifying selection than the C-terminus. The average dN substitution rates of the N-and C-termini were approximately five times greater than that of the core (S3- S5 Figs and S4-S9 Tables).
The pattern of selection varied between the N-and C-termini in the B56-1 and B56-2 subfamilies (Fig 1). The B56-1 subfamily displayed stronger purifying selection at its N-terminus as compared to its C-terminus, whereas the C-terminus of the B56-2 subfamily displayed stronger purifying selection than its N-terminus. The termini likely bind to molecules that differentially interact with the individual isoforms, e.g., their substrates. This suggests that subfamily-specific protein partners interact with the N-termini in the B56-1 subfamily and the C-termini in the B56-2 subfamily. On the other hand, specificity within the subfamilies was likely encoded by the C-termini in the B56-1 subfamily and the N-termini in the B56-2 subfamily, with these domains providing binding sites for paralog-specific protein partners. This finding correlates with the existence of a B56-2 mixed-isoform splice variant of the B56γ locus, B56δ/γ [3,23]. This N-terminal alternative splicing is likely to lead to a gene product possessing a function distinct from B56γ, whereas an alternative splicing of the Cterminus would not. The presence of the B56δ/γ splice variant may have allowed the loss of the B56δ gene in Xenopus, as it would likely possess functions more similar to B56δ than B56γ.
Individual B56 isoforms. Within each B56 isoform, the strong purifying selection of the core was tempered by the weak purifying or slight diversifying selection of the N-and C-termini to yield moderately strong purifying selection of the entire gene (Fig 2). Analyses of the larger B56 groups showed diversifying selection in the termini, as the termini were likely to have been hot spots for amino acid substitutions that define the individual B56 isoforms. The N-and C-termini showed relatively similar selective pressures in each of the B56 isoforms. However, the N-termini of B56α and the C-termini of B56β had ω ratios closer to neutral, or greater than one in the case of B56β. The weak purifying or slight diversifying selection in the N-termini of B56α and the C-termini of B56β suggests that these domains were not as restricted in their amino acid substitutions as other domains of the protein or as the N-and Ctermini of B56γ, B56δ, B56δ/γ, or B56ε. This also suggests that B56α and B56β possess more varied functions, or that they interact with proteins that are not as highly conserved as the proteins interacting with B56γ, B56δ, B56δ/γ, or B56ε in the species analyzed. B56β possessed the highest ω ratios for every domain except for the N-terminus, while B56ε possessed the lowest ω ratios of any other isoform (S2-S5 Figs). The strong purifying selection of B56ε may be due to the fact that it is the only isoform known to be required for Wnt signaling, while most other isoforms inhibit Wnt signaling [4][5][6].
The dS substitution rates of B56δ were approximately two-fold higher than the other paralogs. Differential dS substitution rates for B56δ were maintained when the whole sequence, the core, or the N-terminus were analyzed, but not the C-terminus (S2-S5 Figs; S2, S5, S7 and S9 Tables). The majority of B56 isoforms may contain regulatory elements of gene expression that fall in the coding region and are manifested in codon bias and therefore low dS rates. However, B56δ's high dS rates suggest that its gene expression may be regulated by mechanisms that are independent of codon bias.

Selective pressure experienced by B56 subdomains
The three dimensional structure of B56γ was determined while complexed with the PP2A AC heterodimer, as well as alone [33][34][35]. The core domain forms eight pseudo-HEAT repeats, each of which consists of two antiparallel α-helices of four to nineteen amino acids. The α-helical pairs are separated by five to twenty-eight amino acids, while the individual pseudo-HEAT repeats are separated by one to eleven amino acids. In these X-ray crystallography studies, the N-and C-termini were either not present in the protein that was crystallized, or were not visible in the resultant crystalline structure due to their flexible nature. The reduced purifying selection that we have shown in the termini is likely due to this flexibility. This analysis was carried out with the expectation that subdomains involved in interactions with the A and C subunits would be shared by all paralogs and therefore would have experienced strong purifying selection. However, subdomains determining paralog specificity, i.e., substrate specificity and/ or subcellular localization, would have experienced weak purifying or diversifying selection. To define the selective pressures felt by discrete regions within the B56 gene family at a finer resolution, a sliding window analysis was used to examine dN substitution rates, dS substitution rates, and ω ratios.
B56 family and subfamilies. dS substitution rates were between one and two throughout most of these analyses (Fig 3). When all B56 sequences were analyzed together, both the Nand C-termini provided an exception to the moderate dS substitution rates, as their dS values dropped dramatically to approximately 0.2 at the N-terminus and 0.25 at the C-terminus. In examining the B56-1 and B56-2 subfamilies, it appeared that the low dS substitution rate at the N-terminus of all B56 sequences was due to the low dS substitution rate at the N-terminus of B56-2, which had a dS substitution rate of 0.15. On the other hand, the low dS substitution rate at the C-terminus appeared to be due primarily to the 0.3 dS substitution rate at the C-terminus of B56-1. The low dS substitution rates at the termini were likely due to the effects that synonymous substitutions can have on gene expression [31]. Specifically, intragenic variation in synonymous codon usage can affect the rate of translation and therefore the dynamics of protein folding, especially for proteins containing α-helical domains [36]. The B56-2 subfamily had lower ω ratios than the B56-1 subfamily, especially in subdomains in the central core domain, signifying that specific subdomains experienced stronger purifying selection than the corresponding regions in B56-1.
Individual B56 isoforms. The dramatic drops in the dS substitution rates at the N-and Ctermini were not observed in the B56 paralog analyses, indicating that the depressions detected in the entire B56 family may be due to species-specific codon bias (Fig 4). Indeed, dS substitution rates were reduced at the N-and C-termini in species-specific analyses of the B56 paralogs (Fig 5) Codon bias has been detected in vertebrates; it is often gene-specific, and tends to be observed in highly expressed genes [37,38]. This suggests that B56 genes undergo species-specific regulation of their gene expression. The increased dS substitution rates of B56δ, as mentioned earlier, were also apparent. The most significant deviation of dS substitution rates occurred in an approximately 100 amino acid region in the N-terminal half of the B56ε core (amino acids 25-150 of the core), where it dropped down to an average of approximately 0.3. Similar depressions in dS substitution rates in the N-terminal end of the core were also present in B56α, B56γ, and B56δ/γ, although they were not as deep or as extensive as that in B56ε. Overall, these reductions in synonymous substitutions were most likely due to the effects that intragenic synonymous changes can have on gene expression [36].
No regions of strong purifying selection were shared by all B56 paralogs. B56γ, B56δ/γ, and B56ε displayed ω ratio depressions, indicating strong purifying selection, in pseudo-HEAT repeats one and seven; B56β, B56δ, and B56ε in repeat two; B56δ and B56ε in repeat three; B56γ and B56δ/γ in repeats four and five; all paralogs but B56β in repeat six; and B56α and B56γ in repeat eight. None of the regions of purifying selection were present in all paralogs, and the depressions did not segregate with the subfamilies. This suggests that prior to duplication, the ancestral B56 gene had multiple conserved regions that were subject either to subfamilyspecific relaxation of selection or subfamily-specific selection for novel functions [16]. Alternatively, the regions of purifying selection could have arisen independently in the paralogs, or these regions could have been copied from one paralog to another through gene conversion.

Selective pressure on individual amino acids
To further delineate regions of purifying and diversifying selection in the B56 gene family, we analyzed ω ratios of individual amino acids. Whether all B56 genes, the two subfamilies, or individual isoforms were analyzed, a preponderance of three distinct ω ratios resulted: 0, 0.65, and 1.9, indicating strong purifying selection, moderate purifying selection, and diversifying selection, respectively (Figs 6 and 7). The congregation of ω ratios at 0.65 and 1.9 suggests that the residues within each of these two groups may be coevolving with one another. A study of   Rubisco found that half of all residues coevolved and that the coevolving amino acids were in groups of two to sixteen residues that were localized near one another in the three dimensional structure of the protein [39]. Future studies will analyze the possibility of coevolving amino acids in B56.
As mentioned previously, B56's pseudo-HEAT repeats consist of two antiparallel α-helices surrounding an intra-repeat loop; the pseudo-HEAT repeats are connected to one another through inter-repeat loops. Several contact points between B56γ and the PP2A AC heterodimer, primarily in the intra-repeat loops, have been identified [33,34]. Because of the high conservation within the B56 gene family, analogous interactions are likely to occur with each B56 paralog. We found that four out of five contact points with calculable ω ratios, i.e., their dS substitution rates were not zero, had an ω ratio of zero (Fig 7). All but one of these was in an intrarepeat loop. These sites included PP2A A interaction sites K217 in B56α and K273 in B56ε, and PP2A C interaction sites V288 in B56β, H415 in B56δ, and K273 in B56ε. This indicated that B56 contact points with the PP2A AC heterodimer experienced intense purifying selection. However, amino acid 369 in B56β, which is in a position known to interact with PP2A C, had an ω ratio of 1.89, signifying diversifying selection. We have shown, however, that B56β experienced the weakest purifying selection of any of the paralogs (Fig 2).

Conclusions
The B56 gene family has experienced purifying selection, however, individual paralogs experienced even stronger purifying selection, suggesting that the individual isoforms have undergone distinct paths of purifying selection. In addition, the B56 core experienced stronger purifying selection than the N-and C-termini. Within the B56-1 subfamily, the N-terminus experienced purifying selection, while the C-terminus experienced diversifying selection. This pattern was reversed in B56-2. This suggests that the N-termini carry out subfamily-specific functions and that the C-termini carry out isoform-specific functions in B56-1, with the opposite patterning in B56-2. In addition, species-specific reductions in synonymous substitutions at the N-and C-termini suggest that species-specific codon bias may play a role in regulating B56 expression. As B56 isoforms can have opposing roles in signaling pathways, understanding the basis of this antagonism may help to clarify their disparate functions. The divergence of the B56 gene family in vertebrates is likely to have provided a mechanism to finely regulate diverse cell-cell signaling pathways.  Fig. Structural Overlay of dN Substitution Rates Calculated on Individual Amino Acids for Each B56 Isoform. dN substitution rates were calculated for each amino acid, and the average was plotted with 95% confidence interval brackets. Each B56 isoform (B56α, B56β, B56γ, B56δ/γ, B56δ, and B56ε) was analyzed. Dashed lines demarcate the core domain. The data were superimposed on a pictorial representation of the structure of the B56 conserved core domain with each number above a lightly hued bar denoting a pseudo-HEAT repeat and the darker bars within those representing α-helices. (EPS) S7 Fig. Structural Overlay of dS Substitution Rates Calculated on Individual Amino Acids for Each B56 Isoform. dS substitution rates were calculated for each amino acid, and the average was plotted with 95% confidence interval brackets. Each B56 isoform (B56α, B56β, B56γ, B56δ/γ, B56δ, and B56ε) was analyzed. Dashed lines demarcate the core domain. The data were superimposed on a pictorial representation of the structure of the B56 conserved core domain with each number above a lightly hued bar denoting a pseudo-HEAT repeat and the darker bars within those representing α-helices. (EPS) S1 Table. dN, dS, and dN/dS Values for the Analysis of the Entire B56 Gene. The means and standard deviations from dN/dS analyses for the family-wide, B56-1, B56-2, and individual isoform groupings are provided. (DOCX) S2 Table. p Values for the Analysis of the Entire B56 Gene. p values from dN/dS analyses for the family-wide, B56-1, B56-2, and individual isoform groupings are provided. p values less than 0.05 are highlighted in yellow. (DOCX) S3 Table. Domain Positions in the Aligned B56 Sequences. The span of the N-terminus, core, and C-terminus for the family-wide, B56-1, B56-2, and individual isoform groupings from alignments generated in dN/dS analyses are provided. (DOCX) S4 Table. dN, dS, and dN/dS Values for the B56 Core. The means and standard deviations from dN/dS analyses for the family-wide, B56-1, B56-2, and individual isoform groupings are provided. (DOCX) S5 Table. p Values for the Analysis of the B56 Core. p values from dN/dS analyses for the family-wide, B56-1, B56-2, and individual isoform groupings are provided. p values less than 0.05 are highlighted in yellow.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
DNA-Microarray-based Genotyping of Clostridium difficile

Background Clostridium difficile can cause antibiotic-associated diarrhea and a possibility of outbreaks in hospital settings warrants molecular typing. A microarray was designed that included toxin genes (tcdA/B, cdtA/B), genes related to antimicrobial resistance, the slpA gene and additional variable genes. Results DNA of six reference strains and 234 clinical isolates from South-Western and Eastern Germany was subjected to linear amplification and labeling with dUTP-linked biotin. Amplicons were hybridized to microarrays providing information on the presence of target genes and on their alleles. Tested isolates were assigned to 37 distinct profiles that clustered mainly according to MLST-defined clades. Three additional profiles were predicted from published genome sequences, although they were not found experimentally. Conclusions The microarray based assay allows rapid and high-throughput genotyping of clinical C. difficile isolates including toxin gene detection and strain assignment. Overall hybridization profiles correlated with MLST-derived clades. Electronic supplementary material The online version of this article (doi:10.1186/s12866-015-0489-2) contains supplementary material, which is available to authorized users.

Background
Clostridium difficile is a component of the human colonic flora. If the physiological bacterial flora in the colon is altered or damaged by antibiotics, especially by clindamycin, fluoroquinolones, cephalosporins, or amoxicillin/clavulanic acid [1,2], C. difficile is able to multiply and to cause damage due to its production of several toxins. Resulting conditions are antibiotic-associated diarrhea and pseudomembranous colitis (for a recent review, see [1]). Severe cases might progress to toxic megacolon and end fatally [3].
Important virulence factors are secreted toxins TcdA and TcdB, encoded by genes tcdA and tcdB [4] that form a pathogenicity locus together with regulatory genes (tcdC and tcdD) and a gene (tcdE) encoding a holin-like pore-forming protein [5]. TcdA and TcdB irreversibly modify GTPases from the Ras superfamily resulting in disruption of vital signaling pathways of the cell and in cell death [4]. Besides, some C. difficile strains harbor a binary toxin encoded by cdtA and cdtB. The binary toxin appears to modify actin via its ADP-ribosyltransferase activity. Its clinical significance is not yet fully elucidated [4,6,7] The therapy of C. difficile infection includes rehydration, discontinuation of antibiotics triggering the condition, oral administration of vancomycin or metronidazole as well as surgical intervention in severe cases [1]. Relapses are common, either due to surviving spores, or to re-infection. A possible role of probiotics is still investigated as well as the concept of transplanting feces in order to restore the physiological flora [8,9]. With increasing numbers of patients who receive long-term, broad-spectrum antibiotic therapies, C. difficile became an increasingly important problem in healthcare. Case numbers as well as fatality rates are increasing; with the latter being attributed to the emergence of more virulent strains [10].
Transmissions of C. difficile and even outbreaks within hospital settings are common, given that spores are able to survive in a clinical environment and are resistant to alcoholic disinfectants [1]. Hospitalizations, or residence in nursing homes, are significant risk factors for acquisition of C. difficile, and 50 % of patients who stayed in hospital for more than one month acquired C. difficile [11]. Transmissions within healthcare setting justify infection control measures, in analogy to, e.g., methicillin-resistant S. aureus. Besides barrier nursing, isolation, disinfection, etc., this also should include molecular typing in order to trace chains of infections. A variety of methods that included multilocus sequence typing (MLST), sequencing of the slpA gene, multilocus variable-number tandem-repeat analysis and ribotyping has been described previously [12][13][14][15][16][17] and genome sequencing might become an option in the future.
Microarray-based rapid typing proved to be a convenient tool for MRSA genotyping [18] allowing both, virulence and resistance gene detection and molecular typing within one experiment. Therefore, a microarray-based assay was designed to prove this concept for C. difficile.

Profile-and MLST based clade assignment
Data for a subset of most relevant target genes are presented in Table 1; full data are provided in the Additional file 1.
Isolates were clustered into hybridization profiles (HP) or strains based on overall hybridization profiles with emphasis to tcdA/B and slpA alleles. Isolates or strains were regarded as one HP in case of at least 88 % identity of positive/ambiguous/negative classifications for all probe positions covered, plus presence of identical tcdA/ B and slpA alleles. Possibly mobile resistance markers were counted for the score, but they were, contrarily to tcdA/B and slpA, not considered for the definition of hybridization profiles or strains. It still needs to be clarified whether these genes could be used as subtyping markers for isolates within one HP (i.e., for outbreak investigations).
Applying this approach, tested isolates and reference strains clustered into 37 distinct hybridization profiles (HPs; Table 1 and Fig. 1). Three additional profiles were predicted from published genome sequences, although they were not found experimentally. If several isolates with identical hybridization profiles were subjected to MLST, they yielded identical or related sequence types. Occasionally, several ribotypes (RTs) were observed within one cluster and some ribotypes were present in different, although similar or related, clusters.
In C. difficile, MLST-derived sequence types (STs) cluster into five major clades [19]. Hybridization profiles also can be clustered into these clades when analyzing their similarities (see Fig. 1).
Clade I encompasses a variety of sequence types including ST-03, ST-45, ST-54 and others [19]. It was found to correspond to the largest and most diverse cluster of hybridization profiles (HP) comprising HP-1 to 30.
Clade II comprised ST-01/RT-027 strains [19]. It matched hybridization profiles 31 and 32. Beside reference strains, only two isolates were assigned to this Clade indicating that the emergence and spread of ST-01/RT-027 strains [20,21] did not yet engulf the Dresden region at the time when the samples were taken.
Clade III includes ST-05/RT-023 strains [19] corresponding HP-33 and −34. Clade IV consists of ST-37/ RT-017 or HP-35 and -36 strains while a Clade V include ST-11/RT-078 corresponding to HP-37 to HP-39. ST-127-like STs might form an additional clade according to eBurst analysis (with ST254 as predicted founder), putatively named "Clade VI" herein. It included the genome sequence of Strain 6503 (GenBank prefix ADEI) which translated into a 40th hybridization profile. It was not identified experimentally.
In the visualization using SplitsTree (see Methods as well as Fig. 1), the tcdA/B negative isolates appear to form a separate clade. This, however, can be regarded as an artifact related to the relatively high number of probes recognizing the tcd locus (see Discussion).

Alleles of slpA
The gene slpA encodes the surface layer protein. Fifty four probes were designed to distinguish slpA alleles that are currently represented in GenBank, with one or two probes recognizing one allele. Table 2 shows the predicted patterns and the respective GenBank entries as well as the corresponding ribotyping and/or MLST data for isolates identified within this study. The analysis predicted twenty-eight patterns; twenty-one were found. Additionally, two patterns were observed which probably represent truncated variants of known alleles.
Five isolates (2.1 %) yielded no positive slpA signals. Based on their overall hybridization profiles they clustered into two distinct Clade I strains (HP-06,-30). However in HP-30, ambiguous signals for one probe were observed which might indicate the presence of a truncated variant or divergent allele.
There was no direct correlation of slpA alleles, ribotyping and MLST, with isolates of some ribotypes or STs yielding different slpA alleles.

Alleles of tcdA/tcdB
Four probes allowed distinguishing two tcdA alleles. Both alleles, tcdA R20291 and tcdA CF5 , were found in this study; with the former one being more common and being detected in more diverse lineages. Table 3 shows corresponding GenBank entries, HPs, RTs, MLST types and slpA types. Nineteen isolates were tcdA-negative.
For tcdB, seven alleles were distinguished using nine probes (Table 4), but only three, tcdB R20291 , tcdB 630 and tcdB CF5 , were experimentally identified. Allele tcdB 630 was the most common and widespread one. Nineteen isolates were negative for tcdB; its absence correlated with the absence of tcdA.
Co-localized genes tcdC and tcdE were interrogated with one probe each. They were absent from all tcdA/B-  RT-013, RT-087  -1164  sometimes  ambiguous) RT-029, RT-081, RT-094 Table 1 Detected hybridization pattern types and their association with ribo-and sequence types as well as toxin gene alleles and resistance markers (Continued) Full hybridization profiles are provided as Additional file 2 Asterisk indicates in silico analysis only negative strains, but frequently they yielded also in other isolates negative or ambiguous results. This might be attributed to sub-optimal binding conditions for these individual probes, un-appreciated sequence variation or to a technical problem during probe synthesis, and should in future be overcome by re-design.

Binary toxin
Two alleles of the A component (cdtA R20291 and cdtA 630 ) of the Binary Toxin were theoretically predicted from published sequences as well as experimentally identified with four different oligonucleotide probes. Isolates of RT-023/MLST Clade III yielded an additional pattern for which no matching GenBank entry was identified. It is putatively named "cdtA Clade III " in Tables 1 and 5. For the B component (cdtB), three alleles (cdtB M120 , cdtB R20291 and cdtB 630 ) were distinguishable with six probes. The variant cdtA 630 + cdtB 630 was the most ubiquitous one in accordance to the predominance of Clade 1, although some isolates completely lacked cdtA/B. In Clade 1 isolates, ambiguous signals were frequently detected apparently due to a poor performance of two probes (as discussed above for tcdC and tcdE). Clade II strains harbored a distinct variant, cdtA R20291 + cdtB R20291. Isolates of RT-023 or MLST Clade III yielded "cdtA Clade III " while cdtB signals in these isolates were indistinguishable from the cdtB R20291 allele. Clade V isolates carried cdtA R20291 and a characteristic cdtB allele, cdtB M120. Finally, no cdtA/B was detected in Clade IV isolates, and a "Clade VI" genome sequence (Strain 6503, ADEI) did also not include these genes.

Ubiquitous resistance markers
The gene bcrA, encoding the bacitracin ATP binding cassette transporter BcrA, was present in all C. difficile isolates but four. Three probes could be used to identify three different alleles.
Allele bcrA 630 (GenBank AM180355.1; 767,494 to 768,420;probe 1072) was present in all Clade I and Clade II isolates. Clade V isolates carried allele bcrA NAP07 (GenBank ADVM01000079.1; 10,507 to 11,100;probes 1071 and 1073). Clade IV and VI harbor bcrA CF5 (Gen-Bank FN665652.1; 715,979 to 716,905)which also yielded a signal with probe 1071 while the binding site of 1073 was more similar to the equivalent site in bcrA 630 (differing in one base from bcrA 630 but in five from bcrA NAP07 ). Three tested Clade III isolates appeared bcrA-negative. Since no published genome sequence was available for that clade, it is not clear whether this lineage lacks the gene entirely, or harbors an unknown allele.
The gene lmrB, associated with lincomycin/clindamycin resistance was detected in all tested isolates, and in all published genome sequences analyzed. Two probes were used to identify two different alleles. Allele lmrB 630 (GenBank AM180355.1; 2,893,512 to 2,894,912), was detected in the vast majority of isolates. In isolates associated with Clade V, another allele, lmrB NAP07 (GenBank ADVM01000028.1; 28,036 to 29,436) was found.             Likewise, vatA (synonym sat) encoding a virginiamycin/ streptogramin A acetyltransferase was found ubiquitously, in tested isolates as well as in analyzed genome sequences. Two alleles were differentiated using two probes, vatA-NAP07 (GenBank ADVM01000028.1; 23 to 655) in Clade V isolates and vatA 630 (AM180355.1; 2,576,453 to 2,577,085) in all others.

Variable/mobile resistance markers
The presence of cat (chloramphenicol acetyl transferase), erm(B) (RNA methyl-transferase, conferring resistance to macrolides and clindamycin) and tet(M), encoding tetracycline resistance, was variable. The gene cat was found in 18 isolates (i.e., in 7.5 % of tested strains and isolates). The gene erm(B) was detected in two reference strains, BI-9 and 630, as well as in 78 isolates (30 %). tet(M) was present in two reference strains, M120 and 630, and in 33 isolates (14.6 %). Carriage rates within C. difficile strains were ranging widely, with isolates of certain hybridization profiles (e.g., HP-25 to -27) being virtually always positive for erm(B) and/or tet(M).
For tet(M), five probes reacted in different combinations (Additional file 2). An assignment to alleles was not performed because of several possible sources for error. These might include i) a simultaneous presence of different plasmids in one strain, ii) the existence of chimeric forms (for instance, 5′-and 3′-ends in AJ973139.1, AJ973141.1 and FN665653.1 are identical to ADNX01000070.1 while the middle parts are identical to AM180355.1) and iii) possible irregular patterns for lowcopy number plasmids with an effective target concentration around the detection limit of the linear amplification procedure.

Other markers
Two genes, vncS/vexP1 encoding a histidine kinase and a permease were found to always occur together. Some similar strains (e.g., HP-31 and-32, or HP-35 and -36) could be distinguished by their presence or absence.
Several other markers contributed to specific profile showing different alleles that were uniform within a HP but could vary within a clade (Additional file 2). These included genes encoding septum formation initiation protein (divC), flagellin subunit C (fliC), cell wall proteins 66 and 84.

Discussion
A rapid, reproducible and convenient method for molecular typing of C. difficile was developed. It based on a linear multiplex amplification followed by array hybridization. Target genes were resistance genes localized in published C. difficile genome sequences and toxin genes with their different alleles. In addition to these markers, other genes were selected based on the variability of their presence (e.g., vncS/vexP1) or their sequence (divC, fliC, bcrA, lmrB, vatA, genes encoding cell wall proteins 66 and 84). Alone these genes would not be suitable typing markers but taken together, they can be used to generate stable profiles or fingerprints that allow assignment to clusters or clades as defined by other methods.
Genes that show clade-specific allelic variations also include the toxin genes. Therefore, a topic for a future study could be a possible correlation of toxin alleles and/or of clonal complex affiliations to clinical severity. In order to check whether a possible higher virulence is caused by the actual toxin alleles, or by some other factor linked to phylogenetic background, a high number of isolates from defined conditions need to be typed and their toxin alleles need to be determined. The proposed system might be a suitable platform for such a task.
It can be assumed that ribotyping, slpA typing, MLST and array hybridization yield comparable phylogenetic information, i.e., strains that are recognized as similar/ related by one method will also appear as similar/related by the other methods. However, there is no complete correlation. One ribotype might be associated with two similar array profiles or related MLST types and vice versa. Single and multilocus typing schemes by design tend to emphasize subtle differences. Isolates that are identical belong by definition to the same ST, but single locus variants, and even those that differ in a single base exchange are defined to belong to another ST. STs are numbered chronologically (i.e., by date of submission to the database curator) so that their numbers yield no phylogenetic information. Thus, STs with very different numbers might be still very similar. In order to cluster related STs, clonal complexes (as in, e.g., Staphylococcus aureus, [22]) or Clades [19] were introduced giving a more structured overview on the phylogeny of the target species. In C. difficile there are five major clades, at least one minor clade and several "singletons", i.e., STs that have no known links to others [19].
When converting HPs to a SplitsTree graph, its topology is strikingly similar to a SplitsTree graph of MLST sequences as presented by Dingle et al. [19]. The only significant difference is that all tcdA/B negatives are categorized as one "branch". This is an artifact caused by the high number of probes associated with this locus (n = 15, out of which nine to ten normally are positive). The loss of this locus would thus significantly impact the overall hybridization profile overriding other features affecting a smaller number of probes. Negative results of other markers, such as for slpA, would not have this effect because of the smaller number of probes involved.
With regard to practicalities, a major advantage for the array-based approach is that isolate typing as well as toxin gene detection and allele identification can be performed within one experiment by a single amplification reaction starting from clonal colony material. The amplification follows linear kinetics, utilizing one primer per target. This has the advantage of facilitating unlimited "multiplexing", i.e., the simultaneous detection of multiple targets, and of being resistant to contaminations by amplicons from previous experiments. The disadvantage is a reduced sensitivity compared to standard, exponential PCR. However, since the assay was designed to characterize cultured and cloned bacterial cultures (as opposed to native patient samples) this is not of relevance; and sequencing-based typing methods would also lead to nonsensical results when applied to polyclonal samples. In practical terms, protocol and time requirements, including hands-on-time, of the linear amplification are the same as for normal PCR. The subsequent hybridization procedure can be performed within half a day being more rapid than ribotyping. The assay as well as analysis and interpretation can largely be automatized. The set of probes can, possibly combined with MLST markers and splA sequences, also be mapped to "conventional" or "next generation" sequence data in order to rapidly obtain clinically relevant typing information out of an abundance of data and to create a database that encompasses both, in silico and in vitro typing data.

Conclusions
The microarray based assay allows rapid and highthroughput genotyping of clinical C. difficile isolates including toxin gene detection and strain assignment. Overall hybridization profiles correlated with MLSTderived clades, and target genes that showed cladespecific allelic variations also included the toxin genes.

Ethics statement
Isolates were obtained as part of routine diagnostics and were analyzed retrospectively and anonymously. No patient data were used. Ethical approval and informed consent were thus not required.

Culture and DNA preparation
Isolates were kept frozen at-80°C using cryobank tubes (Microbank, Pro-Lab Diagnostics, Richmond Hill, Canada). Prior to use they were inoculated on pre-reduced Schaedler haemin-cysteine blood agar and incubated at 37°C for 48 hours. Then, harvested culture material was transferred into 200 μl Lysis buffer/enzyme mix (A1 + A2; from Alere StaphyType Kit, Alere Technologies, Jena, Germany). After 60 min incubation at 37°C and 550 rpm, 200 μl AL buffer and 25 μl Proteinase K (from the QIAamp DNA Mini Kit Qiagen, Hilden, Germany) were added and another incubation step of 60 min, at 56°C and 550 rpm followed. After addition of ethanol, DNA was purified using spin columns (QIAamp DNA Mini Kit Qiagen). Finally, DNA was eluted in 50 μl water and heated for 10 minutes at 85°C in order to evaporate trace contaminants of ethanol. The DNA concentration was determined spectrophotometrically at 260 nm. If necessary, DNA was concentrated to 150 ng/μl by evaporation.

Array design
The array was designed to include toxin genes (tcdA/B, cdtA/B), genes related to antimicrobial resistance (cat, erm(B), tet(M)), known typing markers (slpA) as well as genes for which the analysis of published genome sequences showed either a variable occurrence, or the occurrence of distinct alleles. A complete list of targets and primer/probe sequences is provided in Additional file 1. First, all GenBank entries for any given target were retrieved. One entry was selected as reference, and its coding sequence was excised. All resulting BLAST hits were downloaded and re-annotated into a local database excising and aligning all valid open reading frames. Sequences were classified into paralogues and allelic variants based on similarity. Consensus regions from the alignments were chosen for the probe and primer design. Probe sequences were selected for specificity and for similar GC content, length, and melting temperature. Resulting probe sequences were re-blasted against all available sequences to check for false negativity or cross-reactivity.
One hundred thirty-five probes were spotted in triplicate on arrays that were mounted into ArrayStrips (http:// alere-technologies.com/en/products/lab-solutions/platform-components/arraystrip-as.html). The length of the probes ranged from 24 to 34 bases (mean length, 27 bases; median length, 28 bases). There were 140 primers. Their lengths ranged from 18 to 25 bases (mean and median lengths, 20 and 21 bases, respectively).

Protocol optimization
For validation of the array and for the optimization of the protocol, completely sequenced strains (see above) were used. Hybridization profiles were predicted by comparing the probe sequences with their known genome sequences.


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Early outgrowth cells versus endothelial colony forming cells functions in platelet aggregation

Background Endothelial progenitor cells (EPCs) have been implicated in neoangiogenesis, endothelial repair and cell-based therapies for cardiovascular diseases. We have previously shown that the recruitment of EPCs to sites of vascular lesions is facilitated by platelets where EPCs, in turn, modulate platelet function and thrombosis. However, EPCs encompass a heterogeneous population of progenitor cells that may exert different effects on platelet function. Recent evidence suggests the existence of two EPC subtypes: early outgrowth cells (EOCs) and endothelial colony-forming cells (ECFCs). We aimed at characterizing these two EPC subtypes and at identifying their role in platelet aggregation. Methods EOCs and ECFCs were generated from human peripheral blood mononuclear cells (PBMCs) seeded in conditioned media on fibronectin and collagen, respectively. The morphological, phenotypical and functional characteristics of EOCs and ECFCs were assessed by optical and confocal laser scanning microscopes, cell surface markers expression, and Matrigel tube formation. The impact of EOCs and ECFCs on platelet aggregation was monitored in collagen-induced optical aggregometry and compared with PBMCs and human umbilical vein endothelial cells (HUVECs). The levels of the anti-platelet agents’ nitric oxide (NO) and prostacyclin (PGI2) released from cultured cells as well as the expression of their respective producing enzymes NO synthases (NOS) and cyclooxygenases (COX) were also assessed. Results We showed that EOCs display a monocytic-like phenotype whereas ECFCs have an endothelial-like phenotype. We demonstrated that both EOCs and ECFCs and their supernatants inhibited platelet aggregation; however ECFCs were more efficient than EOCs. This could be related to the release of significantly higher amounts of NO and PGI2 from ECFCs, in comparison to EOCs. Indeed, ECFCs, like HUVECs, constitutively express the endothelial (eNOS)—and inducible (iNOS)—NOS isoforms, and COX-1 and weakly express COX-2, whereas EOCs do not constitutively express these NO and PGI2 producing enzymes. Conclusion The different morphological, phenotypic and more importantly the release of the anti-aggregating agents PGI2 and NO in each EPC subtype are implicated in their respective roles in platelet function and thus, may be linked to the increased efficiency of ECFCs in inhibiting platelet aggregation as compared to EOCs.

Background
Endothelial progenitor cells (EPCs) are believed to play a significant role in vascular biology through their implication in vascular repair, cell therapy, and regenerative medicine [1][2][3][4]. Definitive proof of the existence of circulating EPCs was provided in 1997 [5], showing that human peripheral blood contains CD34 + and vascular endothelial growth factor receptor 2 + (VEGFR2 + ) progenitor cells, which can attach to fibronectin, acquire certain characteristics of mature endothelial cells (ECs) in vitro, and contribute to neoangiogenesis in vivo. Since then, numerous cells have been employed to improve cardiovascular function, including EPCs, bone marrow mononuclear cells, myocardial stem cells, mesenchymal stem cells, and embryonic stem cells [6,7]. Among these, EPCs have emerged as important modulators in vascular biology and hemostasis. However, controversies still exist as to their therapeutic and diagnostic use [8][9][10]. One important aspect of EPC biology is their interaction with vascular and blood cells, which can largely influence their functional properties. More specifically, the interaction of EPCs with platelets provides the critical signal to ensure their migration and homing at sites of vascular injury and their differentiation into ECs [11][12][13][14][15][16][17]. Conversely, EPCs affect platelet function [18][19][20][21][22] and modulate endothelial repair [23][24][25][26][27], which add new insights to the importance of EPCs in vascular hemostasis.
In our attempt to elucidate the functional importance of EPC interactions with platelets, we have previously shown that a heterogeneous population of EPCs, derived from PBMCs after 10 days of culture, modulates the function of platelets [18], through binding to platelets via P-selectin [28]. This interaction impairs the function of platelets through an increase in cyclooxygenase-2 (COX-2) expression and prostacyclin (PGI 2 ) release. Recent experimental evidence has established that cells originally defined as EPCs and used for potential therapy are not true endothelial progenitors [8,[29][30][31]. Indeed, recent analyses have revealed that, depending on how EPCs are isolated and cultured, the EPC population can either consist of a minimally proliferative myeloidmonocytic cell population (early EPCs, monocytic EPCs, EOCs, or cultured angiogenic cells) or consist primarily of a highly proliferative non-myeloid EC population (late EPCs, ECFCs, or late outgrowth cells) [9,[32][33][34]. In this regard, we have been characterizing and studying the well-defined EOC subtype and showed that they produce matrix metalloproteinase-9 and reactive oxygen species that influence their pro-angiogenic [23] and anti-platelet functions [19], respectively. On the other hand, ECFCs are considered highly proliferative cells that incorporate at sites of vascular lesions, differentiate into ECs and contribute to endothelial repair [9,32,33,35]. Their anti-platelet properties could be of great importance in vascular hemostasis and repair. Accordingly, this study was designed to describe the characteristics of EOCs and ECFCs, and to compare their effect on platelet function by defining the underlying vasoactive substances implicated in this process. The major findings are that ECFCs are more efficient in inhibiting platelet aggregation than EOCs. This was related to an increase in the production of the potent anti-platelet agents PGI 2 and NO by ECFCs, which induce significantly stronger inhibitory effects of platelet aggregation.

Culture and characterization of human EOCs and ECFCs
This study was carried out according to a protocol accepted by the Montreal Heart Institute ethical committee in agreement with the Declaration of Helsinki. Informed consent was obtained from healthy volunteers aged between 20 and 60 years and medication free over 10 days prior to blood sampling. Ficoll-Paque (GE Healthcare, Piscataway, NJ, USA) density gradient centrifugation was used to isolate peripheral blood mononuclear cells (PBMCs) from 100 mL of peripheral blood, as previously described [18]. To generate EOCs, 1 × 10 6 PBMCs per cm 2 were seeded on 6-well fibronectincoated tissue culture plates (BD Biosciences, Mississauga, ON, Canada) in complete endothelial growth media EGM-2 (Lonza Inc., Burlington, ON, Canada) at 37 °C in an atmosphere of 5 % CO 2 . Following 3 days of culture, the medium was changed to remove non-adherent cells. Additional culturing of adherent cells up to 7 days was allowed to obtain EOCs [23,36]. ECFCs were generated from PBMCs seeded at a density of 5 × 10 6 mononuclear cells per cm 2 in 6-well collagen-coated tissue culture plates (BD Biosciences). Culturing of ECFCs was performed in EGM-2 supplemented with 10 % FBS. The medium was changed daily for 7 days and every other day thereafter until the appearance of endothelial cell colonies (2-4 weeks), which were cultured until the formation of a confluent monolayer of highly proliferative EPCs [36][37][38]. Cells were used between passages three and six. Characterization of EOCs and ECFCs was compared to mature human umbilical vein endothelial cells (HUVECs) (Lonza Inc.) cultured in complete EGM2 with 10 % FBS on 0.2 % gelatin-coated flasks.

Matrigel tube formation assay
EPC's potential to form tube-like structures was compared to that of HUVECs on Matrigel-coated 24-well plates [39]. Growth factor reduced Matrigel (BD Biosciences) (200 μL) was incubated for 30 min at 37 °C in a 5 % CO 2 atmosphere to allow polymerization and formation of a gel-like surface. Cells were harvested with trypsin-EDTA 0.05 % (Gibco, Burlington, ON, Canada), washed and resuspended in complete EGM-2 at 200 × 10 3 per 300 μL per well. Following 24-h incubation at 37 °C in a 5 % CO 2 atmosphere, cells were viewed under an inverted microscope and images were taken at 10× objective magnification.

Cell surface marker expression
Phenotypic characterization of EOCs and ECFCs was performed by flow cytometry and compared to PBMCs and HUVECs [18]. Cells were harvested by washing cells with phenol red-free and serum-free basal RPMI 1640 medium followed by trypsinization with 0.05 % trypsin-EDTA (Gibco) for 15 min at 37 °C [38]. Cells were subsequently blocked with normal mouse serum for 15 min, washed and incubated for 30 min with PElabeled fluorescent human monoclonal antibodies against the monocytic marker CD14 (R&D Systems, Minneapolis, MN, USA), the leukocytic marker CD45 (AbD Serotec, Oxford, UK), the progenitor marker CD34 (BD Biosciences), the angiogenic marker VEGFR2 (R&D Systems), and the endothelial markers CD31 (BD Biosciences) and CD144 (BD Biosciences). Cells were then fixed with paraformaldehyde for 30 min and analyzed on an Altra flow cytometer (Beckman Coulter, Mississauga, ON, Canada). Cells were gated by their characteristic forward and side scatter properties.

Platelet aggregation
Platelets were isolated from 100 mL peripheral blood of healthy volunteers, as previously described [18,19]. Washed platelets resuspended in HBSS-Hank's buffer were then adjusted to 250 × 10 6 /mL. Platelet aggregation was performed in a four-channel optical aggregometer (Chronolog Corp., Havertown, PA, USA) under shear (1000 rpm) at 37 °C. A volume of 400 μL of the washed platelet preparation was incubated with 100 μL of PBMC, EOC, and ECFC cells or their supernatants at 37 °C for 5 min prior to the assay. Platelet aggregation was then induced with 1 μg/mL collagen (Chronolog Corp.), and traces were recorded until stabilization of platelet aggregation was reached. Controls were performed by incubating platelets with complete EGM-2 culture medium.

Western blot
EOCs and ECFCs were harvested using trypsin-EDTA 0.05 % (Gibco), centrifuged at 500×g for 10 min then resuspended in PBS 1× and sonicated. Protein content was assessed by the Bradford assay method, mixed with the appropriated volume of 4× Laemmli loading buffer and heated for 5 min at 95 °C. Protein lysates (40 μg) were resolved by SDS-PAGE and transferred onto nitrocellulose membranes (Bio-Rad, Hercules, CA, USA). Membranes were blocked with 5 % non-fat milk in TBS-Tween-20 for 1 h. Membranes were then incubated overnight with primary antibodies (1:1000) against eNOS and iNOS (Cell Signaling, Beverly, MA, USA), and against COX-1 and COX-2 (Santa Cruz Biotechnology, Santa Cruz, CA, USA). Following washing steps, membranes were labeled with horseradish peroxidase-conjugated secondary antibody for 1 h, washed and bound peroxidase activity was detected by enhanced chemiluminescence (Perkin Elmer Life Sciences, Waltham, MA, USA).

PGI 2 release assay
A commercial radioimmunoassay (RIA) PGI 2 kit was used to assess prostacyclin release (Assay Designs, San Diego, CA, USA) according to manufacturer instructions. Levels of 6-keto-PGF 1∝ , the stable metabolite of PGI 2 , were assessed in EOC and ECFC culture supernatants and compared to PBMC and HUVEC supernatants.

NO release assay
A commercial fluorometric NO assay kit was used to assess nitric oxide release (Cayman Chemicals, Ann Arbor, MI, USA) according to manufacturer instructions. Levels of total nitrate and nitrite (NO 2 − and NO 3 − ) were assessed in EOC and ECFC supernatants and net nitrate (NO 2 − ) concentrations were calculated and compared to PBMC and HUVEC supernatants.

Statistical analysis
Results are presented as mean ± SEM of at least three independent experiments. Statistical comparisons were done using either paired student's t test or a one-way ANOVA followed by a Dunnett's-t-test for comparison against a single group. Data with p < 0.05 were considered statistically significant.

EOCs and ECFCs are morphologically and phenotypically different
Different culture techniques were required to generate each EPC subtype. EOCs were generated from PBMCs cultured for 7 days on fibronectin-coated plates whereas ECFCs were obtained following longer culture periods (2-4 weeks) on collagen-coated plates. The morphology observed for each cell subtype differs greatly. EOCs form a heterogeneous population of round and elongated cells whereas ECFCs form a cobblestone-like monolayer of homogenous appearance (Fig. 1a). EOCs display an immature endothelial cell character since they still resemble the PBMC population from which they derive following 7 days of culture. On the other hand, ECFCs seem to acquire an endothelial-like phenotype resembling HUVECs. Furthermore, EOCs are short-lived cells which do not survive past 7 days whereas ECFCs are highly proliferative cells which can be passaged and kept in culture for months (data not shown). Both EOCs and ECFCs bind Ulex-lectin and internalize DiI-Ac-LDL, both endothelial cell characteristics (Fig. 1b).

EOCs and ECFC tube formation potential
We show that EOCs and ECFCs are also functionally different. EOCs do not form tube-like structures, whereas ECFCs, similarly to HUVECs, form well-structured tubes on Matrigel (Fig. 1c).

EOCs and ECFC cell surface marker expression
EOCs continue to express the leukocytic and monocytic cell surface markers CD45 and CD14, respectively, after 7 days of culture, whereas ECFCs lose these markers throughout their differentiation process. Moreover, EOCs strongly express the angiogenic marker VEGFR2. Unlike EOCs, ECFCs express the progenitor cell marker CD34 and the endothelial cell marker CD144. All cell populations showed uniform expression of the platelet-endothelial cell adhesion molecule CD31 (Fig. 2). Fig. 1 Characterization of PBMC-derived EOCs and ECFCs. a Representative optical microscopy images of EOCs and ECFCs taken at 10× magnification using an inverted light microscope. EOCs display a heterogeneous population of round and elongated cells on fibronectin following 7 days of culture. ECFCs show a homogeneous population forming a cobblestone-like monolayer on collagen following 21 days of culture. b Representative confocal microscopy images showing EOCs and ECFCs triple staining for DiI-Ac-LDL uptake (red), Ulex-lectin binding (green) and TO-PRO-3 nuclear staining (blue) taken at 63× magnification. c Representative optical microscopy images showing the tube-like structure formation potential of EOCs and ECFCs compared to HUVECs on a Matrigel surface taken at 10× magnification using an inverted microscope

EOCs and ECFCs inhibit platelet aggregation
In order to better understand the influence of EPCs on platelets, we sought to investigate the effect of each EPC subtype on platelet aggregation. We show that both EOCs and ECFCs and their respective supernatants inhibit platelet aggregation, in comparison to PBMCs (Fig. 3). However, we noticed an increased efficiency of the supernatants in inhibiting platelet aggregation in comparison to their respective cell samples for both EOCs and ECFCs. We suggest that this may be due to the release of vasoactive agents which play an important role in platelet aggregation. Furthermore, we demonstrated that ECFCs are more efficient at inhibiting platelet aggregation than EOCs.

ECFCs release more NO and PGI 2 than EOCs
To further understand the increased efficiency of ECFCs compared to EOCs in inhibiting platelet aggregation, we compared the NOS and COX expression profiles in EOCs, ECFCs, PBMCs, and HUVECs. We found that ECFCs, similarly to HUVECs, constitutively express eNOS, iNOS, and COX-1, but to a lesser extent COX-2. In contrast, EOCs do not constitutively express these NO and PGI 2 producing enzymes (Fig. 4a). Using radioimmunoassay and fluorometric techniques, we assessed PGI 2 and NO release, respectively. We found that EOCs release minimal amounts of NO (60 pmol) and PGI 2 (300 pg/mL) whereas ECFCs, similarly to HUVECs, release significantly higher levels of both NO (90 pmol) and PGI 2 (5000 pg/mL) (Fig. 4b,c). The higher expression of NOS and COX enzymes in ECFCs account for the significantly higher levels of NO and PGI 2 released by these cells in comparison to EOCs.

Discussion
EPCs have gained much attention in the past decade for their therapeutic role; however, no clear consensus has been achieved to date as to the origin and specific function of these cells. Indeed, the term EPCs represents a complex assortment of progenitor cells that play different role on neoangiogenesis and vascular repair. The characterization and critical re-evaluation of EPC phenotypes are therefore essential for their efficiency as therapeutic and diagnostic tools (for review Fadini et al. [9]). Different conditions such as source, media, incubation times and culture surfaces have yielded different subtypes of EPCs. However, recent evidence suggests the existence of two subtypes of EPCs according to their time-dependent appearance. Based on new findings and on standardized isolation and differentiation protocols [30,34,[36][37][38], we have first successfully differentiated two subtypes of EPCs from PBMCs: EOCs and ECFCs, each distinct in their morphology, phenotype, and function. As it has previously been argued, we have found that EOCs and ECFCs differ in morphology and phenotype since EOCs possess monocytic-like characteristics, whereas ECFCs are rather endothelial-like cells. In addition, EOCs neither proliferate nor form tube-like structures, whereas ECFCs are highly proliferative and form structured and defined tubes on Matrigel. These data confirm previous studies showing that EOCs lack the ability to incorporate into the neovasculature, but rather influence neovascularization through the release of paracrine factors, whereas ECFCs can be incorporated in new vessel formation [36,40]. However, the two EPC subtypes seem to act synergistically in vascular repair [40][41][42]. Therefore, we aimed at studying the role of EOCs and ECFCs in platelet function as the former cannot form new vessels but may considerably influence platelets by releasing a multitude of vasoactive substances, whereas the latter may indirectly influence platelet function while incorporating at sites of vascular lesions. EPCs and platelet interactions occur at sites of vascular lesions in order to promote vascular repair. Being the first cells to be recruited at sites of vascular damage, platelets become activated to release a multitude of paracrine factors (SDF-1) and to express cell surface adhesion molecules (P-selectin), which allow for the recruitment and differentiation of EPCs [14,15,43]. In turn, EPCs also modulate the function of platelets by releasing paracrine factors [18,19], such as PGI 2 and NO, which are the two main endothelium-derived vasodilation substances that display potent anti-platelet properties [44]. In the present study, we showed that both EOCs and ECFCs inhibit platelet aggregation. However, ECFCs are significantly more efficient than EOCs at inhibiting this process. Interestingly, the supernatants from both EOCs and ECFCs inhibit platelet aggregation more efficiently than their respective cells. Next, we compared the levels of PGI 2 and NO released by EOCs and ECFCs, as these two substances are the major endothelium-derived vasodilation agents that play protective and antithrombotic roles in platelet reactivity. We showed that EOCs release smaller quantities of both PGI 2 and NO in comparison to ECFCs. This could be related to the presence of a higher quantity of NO and PGI 2 in the supernatants, which accounts for their increased efficiency in inhibiting platelet function as compared to the cells. In this connection, COX-1 and COX-2 are the enzymes involved in the production of prostaglandins, thromboxane and PGI 2 . Nitric oxide synthases (eNOS, iNOS and nNOS) are the enzymes involved in NO production, among which eNOS and iNOS are mainly responsible for NO production by the endothelium. Different levels of expression and regulation of the COX and NOS enzymes determine the levels of PGI 2 and NO released by different vascular cell types. Interestingly, we found that ECFCs, similarly to HUVECs, constitutively express eNOS, iNOS and COX-1, but to a lesser extent COX-2. Surprisingly, EOCs do not constitutively express these NO and PGI 2 producing enzymes. This could be related in part to a regulatory mechanism that occurs at this early stage of the differentiation process, where EOCs, like other NO and PGI 2 producing cells, need to be triggered to upregulate COX and NOS enzymes. However, further investigation is needed to clarify this issue. In the other hand, the pattern expression of COX and NOS in our ECFC population was close to those found in EPCs generated in our previous study [18]. This could explain why ECFCs release considerably higher amounts of the anti-aggregating agents PGI 2 and NO than EOCs, thereby accounting for their enhanced capacity in inhibiting platelet aggregation. However, the mechanism by which EOCs and their supernatants inhibit platelet aggregation remains to be determined. In addition, further research is required to understand the intracellular mechanisms underlying the differential influence of EOCs and ECFCs on platelet function.

Conclusions
Endothelial progenitor cells are regarded as promising therapeutic tools for vascular repair, tissue engineering, and regenerative medicine. However, the clinical usefulness of these cells is hindered by the existence of diverse subtypes of EPCs which exert diverse therapeutic potential. EPCs generated from the culture of PBMCs have been identified as EOCs and ECFCs depending on the culture conditions. In this study, we determined the characteristics of EOCs and ECFCs, and we defined their impact on platelet aggregation, the essential function in both vascular injury and vascular repair. We found that EOCs and ECFCs display distinct morphological, phenotypical, and functional characteristics. In addition, we demonstrated that both EOCs and ECFCs inhibit platelet aggregation, however; the inhibitory effect of ECFCs was more pronounced than EOCs. These inhibitory effects seem to be mediated by the secretion of the anti-aggregating agents PGI 2 and NO by EOCs and, to a greater extent by ECFCs, thus accounting for their enhanced capacity in inhibiting platelet aggregation. These findings enforce our current understandings of different subsets of EPCs and provide novel insights into the differential role played by EOCs and ECFCs in modulating platelet function during vascular repair. This knowledge would benefit the management of atherothrombosis