Department of Biology, University of Winnipeg, Winnipeg, MB, Canada
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Abstract |
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Key Words: fertilin gene duplication fertilization selection mammals
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Introduction |
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In mammals, genes involved in sex determination such as Sry have shown patterns of rapid evolution that were first attributed to positive selection (Tucker and Lundrigan 1993; Whitfield, Lovell-Badge, and Goodfellow 1993). More recently, it has been suggested that Sry rapid evolution is due to lack of selective constraints or episodic selection between species that have diverged for a long period of time (O'Neil et al. 1997). Positive selection has been proposed based on the low intraspecific polymorphism and high divergence of a gene coding for a protein secreted in the mice saliva and used as a pheromonal signal (Karn and Nachman 1999). The analysis of sequence evolution at DAZ, a candidate gene for male infertility, has recently shown signs of positive selection in primates (Bielawski and Yang 2001). Despite the large number of examples, little is known about the role of selection on the evolution of male and female reproductive genes directly involved in fertilization reactions in mammals. An exception comes from the work by Swanson and collaborators (Swanson et al. 2001b), who have detected the role of selection shaping the rapid evolution of female reproductive proteins expressed in the female's egg zona pellucida.
Mammalian fertilization is a complex process that proceeds through a series of steps involving recognition and binding of the sperm to the zona pellucida, the ability of the sperm to cross the barriers imposed by the egg, and sperm fusing to the egg membrane (McLeskey et al. 1998). Prior to sperm-egg interaction, sperm must undergo a series of modifications while passing through the epididymis, followed by capacitation of the sperm membrane in the female reproductive tract. Members of the ADAM (A Disintegrin and A Metalloprotease) protein family are among a number of candidates that might serve as binding partners for the egg-membrane surface proteins (Huovila, Almeida, and White 1996; Myles and Primakoff 1997; McLeskey et al. 1998) or as epididymal secretory proteins that interact with spermatozoa (Cornwall and Hsia 1997).
Fertilin, an ADAM sperm protein involved in the sperm-egg plasma membrane interaction, has been better characterized than other mammalian sperm proteins. Fertilin is a heterodimeric glycoprotein that was first identified in guinea pig using monoclonal antibodies to sperm surface antigens that could inhibit sperm-egg fusion (Primakoff, Hyatt, and Tredick-Kline 1987). The protein is composed of an alpha and beta subunit with similar domain structures (Blobel et al. 1992; Evans, Schultz, and Kopf 1995; Wolfsberg et al. 1995) and is proteolytically processed during sperm development by removal of the prodomain and metalloprotease domain (fig. 1). Processing of fertilin is crucial for exposing the disintegrin domain that mediates sperm-egg binding and for allowing proper localization of fertilin in the head of mature sperm (Hunnicut, Koppel, and Myles 1997; Cowan et al. 2001). Although the prodomain and the metalloprotease domain of some ADAM proteins act to block or to promote protease activity, these domains have no such role in fertilin (Primakoff and Myles 2000).
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Given our current knowledge on the processing of fertilin during sperm maturation and its potential role in sperm-egg interaction and fertilization I have analyzed the pattern of molecular evolution of both fertilin and ß. This article considers whether the evolution of this protein, given its potential role in sperm maturation and fertilization, has been shaped by positive selection. I then asked whether the distribution of positively selected sites among protein regions is homogeneous as opposed to concentrated within protein regions having a role in sperm-egg interaction and/or sperm maturation.
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Materials and Methods |
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Amino acid sequences were aligned using the global alignment algorithm ClustalX (Thompson et al. 1997) and the local alignment algorithm DiAlign2 (Morgenstern, Dress, and Werner 1996). Amino acid alignments were used to generate nucleotide alignments. Domains were identified within sequences by following the Cavia cobaya domain assignments available from the GenBank sequence entry (Accession numbers: Z11719 and Z11720) and by comparing amino acid sequences to domains derived from two collections, Smart and Pfam, using the NCBI conserved domain search service (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml). In assigning domains, I used the amino acid positions defined by the Cavia cobaya GenBank entries. However, the prodomain and the transmembrane and cytoplasmic tail domains were broadly kept in the analysis as amino-end and carboxy-end, respectively, because they were not identified when comparing sequences to domain databases (fig. 1).
Sequence Analysis: Testing Selection
Positive selection can be inferred from a higher proportion of nonsynonymous than synonymous substitutions per site (dN/dS>1). dN and dS were calculated using the modified Nei-Gojobori Jukes-Cantor method which considers deviations from an equal frequency of transitions (ts) and transversion (tv) substitutions (Nei and Gojobori 1986; Nei and Kumar 2000, pp. 5760). The MEGA2 software (Kumar et al. 2001) was used to calculate the ts/tv ratio (R), and R was used as input in the calculation of dN and dS. Estimates were obtained for the entire aligned sequences as well as for defined domains within sequences.
To detect specific amino acid sites under positive selection among sites potentially experiencing variable selective pressures, I used the likelihood ratio test (Nielsen and Yang 1998; Yang et al. 2000). Using the codemlsites program of PAML (Yang 1997), in which the unit of evolution is the codon, I obtained log likelihood estimates () of a tree topology under models that impose alternative assumptions in terms of rate variation (
= dN/dS) over codon sites. Model 0 (M0) assumes constant
ratio across codon sites, whereas model 3 (M3) assumes different proportions (pi) of discrete classes of sites with different
i ratios. Twice the log likelihood difference (2
) between M3 and M0
estimates provides a test for the existence of rate variation over codon sites (Goldman and Yang 1994; Yang et al. 2000). Another test compares the log likelihood of a tree under model 7 (M7), which assumes a distribution of
values constrained between 0 and 1 (no positive selection) and model 8 (M8) that adds a class of sites with
ratios > 1.0 estimated from the data. Comparing
between these two models depicts the existence of amino acid sites under positive selection (Yang et al. 2000). If the log likelihood test suggests the presence of sites under positive selection, then these sites can be identified by using a Bayesian method to estimate posterior probabilities (P) that particular sites are likely to come from a class with
> 1.0. I used P(
>1) > 0.95 as the lower threshold to identify sites under positive selection.
Sequence Analysis: Gene Duplication
In some species, fertilin has undergone gene duplication, and so it is possible that any signal or lack of signal of positive selection could be a consequence of differentiation between paralogs. I used the baseml program of PAML to reconstruct sequences ancestral to the duplication event under models that assume rate variation over nucleotide sites and different patterns of nucleotide substitutions. All models used here assume nucleotide substitution rates drawn from a gamma distribution (Yang 1993). The more complex model (HKY85) assumes different equilibrium frequencies for the four nucleotides (
i) and different transition/transversion rate ratios (
). A simpler model (F81) is one assuming no differences in transition/ transversion rate ratios (
= 1) or a model (K80) assuming equal equilibrium frequencies for all nucleotides (
i = 1). The simplest model is one in which nucleotide frequencies are equal and there is no difference in transition/ transversion rate ratios (JC69) (Yang, Goldman, and Friday 1994).
The log likelihood of the species tree topology for fertilin was calculated under the different models of nucleotide substitutions, and differences in log likelihood (2
) among models were compared to a
2 distribution with degrees of freedom given by the difference in number of parameters estimated for each model. The model that better explained the data was used to reconstruct a sequence ancestral to the fertilin
I-fertilin
II duplication event. The ancestral sequence was used to test episodes of positive selection at specific codon sites prior to the duplication event.
Comparisons Among ADAM Gene Family Members
I have used the modified Nei-Gojobori Jukes Cantor method available in the MEGA2 software package to compare dN and dS estimates among 14 different ADAM genes whose sequences are available from mouse and human and for which we have information on their tissue of expression. Estimates were obtained for the metalloprotease and the disintegrin domains identified using the NCBI conserved domain search service (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml). The accession numbers of the gene sequences used are shown in parentheses (Mus musculus; Homo sapiens): Adam 7 (AF013107; AF215824), Adam 8 (NM_007403; NM_001109), Adam 9 (NM_007404; NM_003816), Adam 10 (AF011379; XM_007741), Adam 11 (NM_009613; AB009675), Adam 12 (D50411; AF023477), Adam 15 (AB022089; NM_003815), Adam 19 (D50410; AF311317), Adam 21 (NM_020330; AF158644), Adam 23 (NM_011780; NM_003812), Adam 28 (AF153350; NM_021777), fertilin ß (U38806; U38805), Adam 18/ tmdc III (AF167405; AJ133004), TNF (U69613; U69611).
To test for significant differences in dN estimates between genes, I used a t-test with infinite degrees of freedom. The test is similar to that used to detect differences between dN and dS (Nei and Kumar 2000, p. 55) and consists of calculating the differences in the proportion of substitutions per site and weighting it by the standard errors of the estimates. The formula for the test statistics is: Zij = (dNi - dNj) / ( +
)1/2, where dNi and si are the proportion of nonsynonymous substitutions per nonsynonymous site for gene i and its standard error, while dNj and sj are the same estimates for gene j. The same test was used for differences in dS estimates between genes.
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Results and Discussion |
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The pattern of substitutions for fertilin ß shows the disintegrin, cysteine-rich, and EGF-like domains having a higher proportions of synonymous substitutions than other domains. The proportion of synonymous substitutions is particularly high for the disintegrin domain, making the dN/dS ratio the lowest of all. The proportion of nonsynonymous substitutions is also higher for the disintegrin and cysteine-rich domain than others, with the EGF-like domain showing a dN estimate similar to other fertilin ß domains (table 1).
The question remains whether it is possible that the elevated proportions of synonymous changes at domains such as the disintegrin domain of both fertilin and ß might mask the detection of nonsynonymous changes that could have accumulated due to adaptive divergence between species.
Testing for Codon Changes Driven by Positive Selection
Although the overall dN/dS estimate does not indicate a signal of positive selection, it is possible that particular sites within a coding sequence might be under positive selection. Because the assignment of domains based on the Cavia cobaya protein sequence might be considered arbitrary, I have used a site testing approach to search for codon sites under selection. This approach offers the advantage that searches of positively selected sites can be done without a priori information about domains. Once sites are identified, they can be located within a known domain or within unidentified protein regions (fig. 1). It is also possible that if only specific sites within a domain have evolved under the influence of positive selection, a window analysis based on the entire domain will not detect them.
The phylogenetic analysis by maximum likelihood (PAML) (Yang 1997) was used to estimate the likelihood of a phylogeny under models that make alternative assumptions about the dN/dS rate of change among codon sites () (Nielsen and Yang 1998; Yang et al. 2000; Swanson et al. 2001b). The tree topologies used for the analysis are shown in figure 2. The analysis of fertilin
and fertilin ß was run using ClustalX and Dialign2 alignments. For fertilin
, the alignments obtained when using alternative algorithms were not consistent for residues in the carboxy-end region (beyond the EGF-like domain). This creates a situation of uncertainty when trying to determine what codon sites are positively selected. Therefore, I have removed residues beyond the EGF-like domain (fig. 3a), making the results obtained using ClustalX and Dialign2 alignments consistent, before performing phylogenetic analysis by maximum likelihood (PAML).
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Figure 3 shows codon sites of fertilin inferred to be under positive selection when a more stringent 95% posterior probability threshold (P > 0.95) is applied. The figure also shows whether sites are consistently detected as being under positive selection depending on the alignment used. Figure 3a lists the sites detected as being under positive selection when only a partial fertilin
sequence is considered. The results show that regardless of the alignment used, the same sites are detected as being under positive selection and most are within the cysteine-rich domain (parameters of PAML model are those shown in table 2).
Figure 3b shows that when the entire fertilin sequence is used, a very high number (25) of positively selected sites are detected in the carboxy-end region, where the transmembrane and cytoplasmic tail domains of this protein are supposed to be localized. However, most sites in this region are detected as positively selected depending on the alignment algorithm used reflecting the ambiguity in the alignment of the carboxy-end region. The only time when differences between the alignment programs are not obvious is when the sequences are very similar to one another. Although the ambiguity of the alignment does not allow one to single out positively selected codon sites at the carboxy-end of fertilin
, it is interesting that this region of the protein is so highly divergent. In guinea pig, we know that lateral diffusion of the fertilin protein across the cell membrane of sperm is important during sperm maturation and such movement can lead to species-specific localization of fertilin in the sperm head (Hunnicut, Koppel, and Myles 1997; Myles and Primakoff 1997; Cowan et al. 2001). Adaptive differentiation between species in their transmembrane and cytoplasmic tail portion of the protein may define a species-specific pattern of cellular interactions; however quality of the sequence available in GenBank might also be an issue.
The M0 estimated value was 0.37 for fertilin ß, and the log likelihood estimate was
= -11,259.27 (table 2). M3 estimates suggest that most sites in fertilin ß are evolving under strong or mild purifying selection with
0 = 0.08,
1 = 0.71 (p0 = 0.52 and p1 = 0.39), whereas approximately 9% of sites have more than a 0.5 probability of being under positive selection with
2 = 3.75 (table 2). M3 fits the data significantly better than M0, the test statistic is 2
= 625, compared to a
2 value with df = 4 (table 2). The estimated distribution of
values under M7 has most of the sites having
values close to either 0 or 1 B(0.34, 0.50), and the log likelihood estimate under this model was
= -10,987.35. M8 model fits the data better than M7 (2
= 81.2, significantly greater than a
2 value with df = 2) (table 2). The M8 estimates suggest that about 9% of fertilin ß sites (p1) have more than a 0.5 probability of being under positive selection with
= 3.60 (table 3). Figure 3c shows sites within different domains of fertilin ß inferred to be under positive selection at a 95% posterior probability threshold level (P > 0.95) when alternative alignments are used (ClustalX or Dialign2). Results are consistent regardless of the alignment used with positively selected sites being detected only within the disintegrin and the cysteine-rich domains (fig. 3c).
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It is possible that part of the positive selection signal detected for fertilin could be assigned to acquisition of new functions between paralogs or alternatively that strong preservation of function between recently derived duplicates may be masking an ancestral event of adaptive diversification by positive selection.
To determine whether fertilin duplicates have recently diversified in function or preserved an ancestral status, I reconstructed ancestral sequences using models that assume different patterns of nucleotide substitutions. Log likelihood estimates for the fertilin
tree topology (fig. 2) under different models of nucleotide substitution are shown in table 3. Model JC69 appears unacceptable compared to model K80 because the transition/transversion ratio is different from 1 (
= 3.7) (2
= 643.6, df = 1; P < 0.0001) (table 3). Similarly, among models that do not assume equal nucleotide frequencies (
i), model HKY85 is significantly better than model F81 (2
= 638.6, d.f. = 1; P < 0.0001) (table 3). Because nucleotide frequencies are very close to equal for the fertilin
data set (
T = 0.25,
C = 0.24,
A = 0.25,
G = 0.26), model K80 is as realistic as model HKY85 for inferring nucleotide substitutions and ancestral sequences (their log likelihood estimates are almost equal) (table 3). I used the simpler model K80 to reconstruct sequences ancestral to the gene duplication event in fertilin
. A sequence ancestral to the Macaca-Saguinus-Papio fertilin
I and fertilin
II duplication event (see arrow in fig. 2) was used to test for codon sites under positive selection using the codemlsites program of PAML.
Model M8 ( = -9,723.00) fitted the data better than M7 (
= -9,769.90); the test statistic was 2
= 93.8 (df = 2; P < 0.0001), suggesting that positive selection has shaped the evolution of single codon sites between species prior to the fertilin
duplication event. The question remains whether positive selection leading to acquisition of new adaptations among duplicates has been responsible for the pattern of positive selection previously detected (fig. 3a). Compared to the results obtained when using fertilin
I and
II duplicates (fig. 3a), the use of an ancestral sequence shows more sites under positive selection within the disintegrin domain (7) but similar numbers within the cysteine-rich (7) and EGF-like domains (1). The actual sites in the Cavia cobaya reference sequence shown in figure 3a to be under positive selection at a 95% posterior probability threshold level (P > 0.95) when the ancestral fertilin
sequence is used are 476, 508, 510, 520, 535, 543, and 546 (disintegrin domain); 588, 591, 594, 611, 644, 672, and 674 (cysteine-rich domain); and 678 (EGF-like domain).
Therefore, the duplication event of fertilin between Papio, Macaca and Saguinus is not responsible for the positive selection signal detected within the cysteine-rich and EGF-like domains. The result also shows that preservation of function within the disintegrin domain among duplicates might have masked a stronger ancestral pattern of positive selection for disintegrin. A previous study has shown that sequence similarity among these three species is low between fertilin
I and fertilin
II genes for the amino-end (50% to 65% identity) and the carboxy-end (40% identity), whereas they are almost 100% identical in the central region (Perry et al. 1995; Jury, Frayne, and Hall 1998). This level of similarity among duplicates may partially explain the weaker positive selection signal originally detected using the fertilin
I and
II paralogs.
Other Members of the ADAM Gene Family
ADAM proteins with a pattern of expression in reproductive tissues have been implicated in sperm maturation (ADAM7, ADAM21, and ADAM28) and sperm egg fusion (ADAM21, fertilin and ß, ADAM18). Members of the ADAM gene family are also implicated in other functions such as neurogenesis and myogenesis. Previous studies comparing the pattern of molecular evolution of reproductive versus nonreproductive genes have done so by pooling together different genes into these two classes (Civetta and Singh 1999; Wyckoff, Wang, and Wu 2000, Swanson et al. 2001a). This type of approach is particularly sensitive to the gene samples being influenced by characteristics other than their site of expression or function. Studying the pattern of molecular evolution for genes belonging to the same family that have evolved new functions, such as the ADAM genes, offers the advantage that the pool of genes compared is more homogeneous in terms of their evolutionary history.
Fertilin showed high proportion of nonsynonymous substitutions within adhesion domains such as the disintegrin and cysteine-rich domains. An open question is whether this pattern is common to all ADAM genes. In order to answer this question, I estimated dN and dS for a total of 14 different ADAM genes for which sequences were available for mouse and human. The analysis compared the metalloprotease and disintegrin domain. Table 4 shows that in 13 out of 14 comparisons, dS is higher in absolute value for the disintegrin than the metalloprotease domain and the probability of this occurring by chance is P = 0.00085. However, the dN values for the disintegrin and metalloprotease domains are split evenly, with the higher dN observed for disintegrin 7 out of 14 times (P = 0.21). Even though the disintegrin domain seems to accumulate a higher proportion of synonymous substitutions for all ADAM genes considered, the proportion of nonsynonymous substitutions shows a more random pattern and so the elevated accumulation of nonsynonymous changes is not necessarily a reflection of a higher rate of synonymous neutral substitutions. When ADAM genes expressed in reproductive tissues (ADAM 18, ADAM 28, fertilin ß, ADAM 21, and ADAM 7) are compared to others, they show nonsignificant differences in dS estimates (Zij estimates with P values > 0.05). However, a significant increase is observed in the proportion of nonsynonymous substitutions for ADAM 7 and fertilin ß and for ADAMs 18 and 28 (except when compared to ADAMs 8 and 15). ADAM 21 shows a pattern of nonsynonymous substitutions more similar to nonreproductive genes (significantly higher than five and nonsignificantly different from four nonreproductive ADAMs). These results show that a high proportion of nonsynonymous replacements might be a common pattern for most ADAM genes with a potential role in sperm-egg interaction and sperm maturation, but whether positive selection drives the accumulation of nonsynonymous replacements in genes other than fertilin remains an open question.
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Conclusion |
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Different studies have suggested adhesion activity for fertilin domains other than disintegrin. Evans, Schultz, and Kopf (1998) suggested a potential role of the cysteine-rich and/or EGF-like domains in sperm-egg adhesion, and the fusion peptide of the fertilin protein was previously identified within the cysteine-rich domain (Blobel et al. 1992). In this article, domains potentially involved in sperm-egg interactions (disintegrin and cysteine-rich domains) show signs of positive selection for both fertilin
and ß. The sign of positive selection for the disintegrin domain of fertilin
seems to be partially masked due to strong preservation of sequence and perhaps function within this domain after a recent duplication event in the common ancestor to Papio, Macaca and Saguinus.
It remains to be determined whether the extremely high degree of sequence differentiation in the carboxy-end of fertilin (particularly within the transmembrane and cytoplasmic tail domains) is a consequence of different species having evolved very specific patterns of membrane localization of fertilin in mature sperm. If that is the case, why the signal is only detected for fertilin
but not fertilin ß will need to be explained.
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Acknowledgements |
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Footnotes |
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