* Education and Research Centre, St. Vincent's University Hospital, Dublin, Ireland
Department of Medicine, University College Dublin, Dublin, Ireland
Department of Genetics, Trinity College Dublin, Dublin, Ireland
Biology Department, National University of Ireland, Maynooth, Ireland
|| Conway Institute, University College Dublin, Dublin, Ireland
Correspondence: E-mail address: cliona.ofarrelly{at}ucd.ie.
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Abstract |
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Key Words: Adaptive evolution antimicrobial peptide -defensin
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Introduction |
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In humans, six -defensins have been identified and studied to date. Human
-defensins 1 to 4 are localized in azurophilic granules of neutrophils, which has led to them being referred to as human neutrophil peptides (HNP1 to HPN4). The HNPs are the most abundant protein in neutrophils and contribute to the oxygen-dependent killing of phagocytosed microorganisms (Ganz et al. 1985; Singh et al. 1988; Bateman et al. 1991). Recently, HNP1 to HPN3 have been shown to be the major components of a soluble factor secreted from CD8 T-lymphocytes that suppresses HIV-1 replication (Zhang et al. 2002). Human
-defensins (HAD) 5 and 6 are primarily located in the secretory granules of Paneth cells within the crypts of Lieberkühn in the small intestine (Jones and Bevins 1992, 1993; Porter et al. 1997; Salzman et al. 2003b) where they have been implicated in mucosal host defense. Further evidence for the important role these peptides play in mucosal host defense has been shown in a recent study in which transgenic mice expressing HAD5 were completely resistant to Salmonella typhimurium (Salzman et al. 2003b). HAD5 has also been detected in female reproductive tract (Svinarich et al. 1997; Quayle et al. 1998) and in bronchial and nasal epithelia (Frye et al. 2000). Peptides homologous to HAD5 and HAD6 have been found in mouse Paneth cells where they are termed cryptdins (Ouellette et al. 1992, 1994).
-Defensins are encoded as propeptides that require proteolytic processing to become activated. It has been suggested that the proregion is cytoprotective because addition of it to in vitro assays inhibit activity (Ouellette et al. 1999; Wu et al. 2003). Paneth cell trypsin has been identified as the processing enzyme for HAD5, but the processing enzyme for HAD6 has yet to be identified (Ghosh et al. 2002). In mice, activation of cryptdins is mediated by matrix metalloproteinase-7 (MMP-7) (Wilson et al. 1999; Ayabe et al. 2002). The signal peptide and proregion in
-defensins exhibit more amino acid conservation than does the mature antimicrobial peptide region. Indeed only the six cysteine residues and a glycine residue at position 18 of the mature peptide are conserved between all
-defensins. These residues are important for the structure of the molecule (Hill et al. 1991), whereas the other residues are free to vary.
Most molecular variation either within species or between species is caused by the random fixation of mutations that are neutral. Mutations that are deleterious to an organism are removed by purifying selection. Occasionally, mutations confer selective advantage to the organisms having them, and, therefore, these mutations are fixed in the population by positive selection (adaptive evolution) at a higher rate than expected under neutral evolution. One of the most stringent methods of detecting adaptive evolution is to compare the rate of nonsynonymous substitutions (dN) with the rate of synonymous substitution (dS). The ratio between these rates ( = dN/dS) is then a reliable measure of the selective pressure acting in a protein-coding gene. If amino acid changes are neutral, they will be fixed at the same rate as synonymous mutations (
= 1). If mutations (dN) are deleterious, dN/dS = 0, and the mutations will be removed by selection. If nonsynonymous mutations are slightly deleterious
is less than 1, and the coefficient of selection acting against the mutation will depend on the population size. Finally, if the amino acid changes are selectively advantageous, they will be fixed at a higher rate (
> 1). A number of methods have been developed to calculate dN and dS among lineages (see for review, Yang [2002]). Maximum-likelihood (ML) methods are most statistically satisfactory because they employ an explicit model of evolution, taking into account the effects of unequal transition and transversion rates, unequal base and codon frequencies, and variable
values among lineages in a phylogeny (Yang 1998; Yang and Nielsen 1998). As most amino acid sites are expected to be conserved to maintain key structural and functional characteristics of a protein, and only a small number of sites in a protein are likely to be affected by adaptive evolution, methods that average substitution rates over all sites in a sequence have little power in detecting cases of positive selection. Apart from models that detect positive selection among lineages, models that can detect amino acids sites under adaptive evolution have also been developed (Nielsen and Yang 1998; Yang et al. 2000).
Because -defensins are a critical component of the innate immune response in an "arms race" against fast-evolving microbes, it is possible that these molecules are subject to adaptive evolution. It has been previously demonstrated that
is greater than 1 in the antimicrobial peptide region of
-defensins, indicative of positive selection (Hughes and Yeager 1997). The Hughes and Yeager study was, however, unable to predict which particular amino acid sites were under positive selection, as appropriate models were undeveloped at that time. In this first comprehensive study of all available mammalian
-defensins, we implement ML models to test for positive selection among evolutionary lineages and among amino acid sites.
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Materials and Methods |
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Variable Selective Pressures Among Amino Acid Sites
Models of variable ratios among sites were used to test for the presence of sites under diversifying selection (
> 1) and to identify them. Five models for the
distribution implemented in the CODEMLSITES program of the PAML package were tested. Model M1 (neutral) assumes two classes of sites in the protein: the conserved sites (
= 0) and the neutral sites (
= 1). Model M2 (selection) adds a third class of site, with
as a free parameter, allowing for sites with
greater than 1. Under the discrete model M2, the proportion of sites under purifying selection (p0) and proportion of sites under neutrality (p1) are estimated from the data. Model M3 (discrete) uses a general discrete distribution with three classes of site, with the proportions (p0, p1, and p2) and the
ratios (
0,
1, and
2). Model M7 (beta) uses a beta distribution, which, depending on parameters p and q, can take different shapes in the interval (0, 1). Model M8 (beta and
) adds an extra class of sites to the beta (M7) model, with the proportion and the
ratio estimated from the data, thus allowing for sites with
greater than 1. From these models, three LRTs compare M0 (one ratio) with M3 (discrete), M1 (neutral) with M2 (selection), and M7 (beta) with M8 (beta and
), respectively. Models M2, M3, and M8 are tests of positive selection among sites. Posterior Bayesian probabilities of site classes were calculated for each amino acid site. If the
ratios for some site classes are greater than 1, sites with high posterior probabilities for those classes are likely to be under positive selection.
Sliding-Window Analysis to Detect Selective Constraints by Maximum Parsimony
Several studies have shown that maximum-likelihood methods are sensitive to the violation of assumptions made in models to detect adaptive evolution and that false positive results could be obtained under certain conditions (Suzuki and Nei 2002). We, therefore, applied a maximum-parsimony method to test for adaptive evolution in our sequences. We applied the Kimura-based model of Li (1993) using a sliding-window procedure (Fares et al. 2002). Briefly, the method infers a statistically optimum codon-window size and slides it along the alignment. We then test in each sliding step the significance of the nonsynonymous nucleotide substitutions (dS), synonymous substitutions (dN), and the nonsynonymous-to-synonymous rate ratio (). The mathematical approach used is based on the maximum-parsimony method of Suzuki and Gojobori (1999). The main difference however resides in the fact that the window size is selected under a statistical procedure (Fares et al. 2002). Another advantage of using this method is that the number of synonymous substitutions is tested for significance, and, hence, saturated synonymous sites, if any, can be highlighted and removed from the analysis.
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Results |
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Multigene families whose members have the same function may evolve in a concerted fashion that homogenizes the sequences of the member genes by interlocus recombination or gene conversion, such that sequences within a species are more similar to each other than those between species (Liao 1999). The physical clustering typical of -defensin families within species may imply concerted evolution. There are, however, two exceptions to phylogenetic clustering in our tree (fig. 1), which may mean that the birth-and-death model of evolution is more appropriate (Nei, Gu, and Sitnikova 1997). Neither the primates nor the rodents show species-specific clades of
-defensins. In fact, in the case of the mouse and rat, the phylogenetic clustering appears to be caused by a functional relationship between the different
-defensins. Mice do not have neutrophil defensins (Eisenhauer and Lehrer 1992) and most of the rat
-defensins are expressed in neutrophils; however, the one rat
-defensin expressed in the intestine (ED_Rn) in a similar manner to the mouse cryptdins, clusters with the mouse
-defensins. Furthermore, the level of sequence divergence, even at the protein level (fig. 1 in supplementary material available online), is inconsistent with the homogenization of sequences expected under the concerted evolution model. In the birth-and-death model of evolution, duplicate genes are produced in a gene family. Some of the genes functionally diverge, others may be lost from the genome, and still others become pseudogenes (Liao 1999). Consistent with this model, we have detected multiple
-defensin pseudogenes in the human and mouse genomes (unpublished data).
Examining Saturation of Synonymous Sites
Saturation of synonymous sites is an important issue in the detection of positive selection by the criterion that dN/dS is greater than 1, as saturation will lead to the underestimation of dS and an inflation of the dN/dS ratio. To ensure that saturation was not an issue in our data set, pairwise dN and dS values were calculated using the ML method implemented by CODEML (Goldman and Yang 1994). A correlation analysis between dN and dS was performed using SPSS version 11.0 (fig. 2). If synonymous sites are saturated, we might expect a quadratic model with an increasing slope to be better fit to the data than a linear model. If synonymous sites are not saturated, a linear model should fit the data better than a quadratic model. A quadratic model with a decreasing slope implies that when average values of nonsynonymous nucleotide substitutions are examined, most of them show strong purifying selection and no saturation of synonymous sites exists in our data that could inflate dN/dS values.
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Many of the sites detected to be under positive selection using ML-based models were also detected using the sliding-windowbased method. Some others, however (amino acid sites 62, 63, 68, 69, 78, and 82), did not show significant differences between dS and dN. Among the positive-selected amino acid sites, the average value was 2.972, being significantly higher than 1 and higher than the expectation under neutrality, even after correcting for multiple tests (multiple sliding-window tests) using Bonferroni correction (Z = 11.47; P < 0.001). We have not detected saturation of synonymous sites and hence
values are not inflated by this bias. We have to stress, however, that maximum-parsimony methods are very conserved and may be subject to the problem of possible convergences. Despite this fact, our results using maximum parsimony are quite coincident with those using ML methods and do not affect the final conclusions of this study.
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Discussion |
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Small changes in the primary structure of these molecules can have enormous effect on their potency. HNP1 and HNP3 differ only by one residue at the N-terminus of the mature peptide, and, yet, HNP1 exhibits potent activity against Candida albicans, whereas HNP3 has little effect. (Lehrer et al. 1988; Raj, Antonyraj, and Karunakaran 2000). This site was also predicted to be subject to positive selection in our study. Similarly, the activity of mouse cryptdin 4, the most potent of the mouse -defensins against a broad spectrum of microbes (Ouellette et al. 1994), is dependent on the presence of one or two residues at the N-terminus of the active peptide. Removal of these residues can totally eliminate the antimicrobial activity of cryptdin 4 (Ouellette et al. 2000).
It is probable that the evolution of different mechanisms of interaction with microbial membranes has been important in the evolution of specificity of particular -defensins for particular microbes. Human HNP2 forms stable multimeric pores in model membranes (Wimley, Selsted, and White 1994), whereas rabbit NP-1 does not, but permeabilizes membranes by creating large, short-lived defects (Hristova, Selsted, and White 1996). These alternative mechanisms are likely to have varying effects, depending on the constitution of the membrane, which is variable among microbes.
Many microbes have evolved mechanisms that attempt to evade and subvert the actions of antimicrobial molecules (Ganz 2001). It is likely that this ongoing "arms race" with microbes has been a significant force driving the adaptive evolution of the -defensins. For example, the human pathogens, Pseudomonas aeruginosa, Enterococcus faecalis, and Streptococcus pyogenes release dermatan sulphate, a compound that binds to and neutralizes HNP1 (Schmidtchen, Frick, and Bjorck 2001). Furthermore, the pathogen Salmonella enterica can elicit a decrease in the expression of mouse cryptdins (Salzman et al. 2003a).
In this study we have provided evidence that several amino acid sites in the active peptide of mammalian -defensins are under positive Darwinian evolution. This work will assist in the design of in vitro analyses of functionally and structurally relevant sites. By synthetically changing the residues at sites predicted to be under positive selection, which are the sites most likely to be of functional importance, it is possible to alter the activity of these molecules against particular pathogens and gain insight into their mechanisms of activity.
Positive selection in the mature antimicrobial region of other antimicrobial peptide families has also been demonstrated. Evidence of positive selection has been detected in primate ß-defensins (Boniotto et al. 2003; Semple, Rolfe, and Dorin 2003), murine ß-defensins (Morrison et al. 2003), the Drosophila andropin antibacterial peptide (Date-Ito et al. 2002), and in a number of amphibian antimicrobial peptides (Duda, Vanhoye, and Nicolas 2002). The detection of positive selection in these important modulators of the innate immune response provides evidence that despite the evolution of the adaptive immune response in jawed vertebrates more than 450 MYA, the innate immune response continues to function as a critical element in host defense.
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Acknowledgements |
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Footnotes |
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