* Department of. Integrative Biology, Department Microbiology and Molecular Biology,
Department of Physiology and Developmental Biology, and
Department of Computer Science, Brigham Young University, Provo, Utah
Correspondence: E-mail: david_mcclellan{at}byu.edu.
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Abstract |
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Key Words: Molecular adaptation cytochrome b Artiodactyla Cetacea protein evolution physicochemical amino acid properties
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Introduction |
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Putative Adaptation of the Cetacean Cytochrome b Protein
Integral membrane proteins of the mitochondrial cytochrome bc1 complex carryout electron transfer in the Q-cycle mechanism of mitochondria (Degli Esposti et al. 1993; Zhang et al. 1998). Cytochrome b (cyt-b), a key transmembrane structure of this complex, is its central catalytic protein and, thus, a key component of the cellular respiratory function. The active sites of cyt-b, to a great extent, establish the proton gradient that fuels the production of ATPs, thus, greatly influencing the overall metabolism of the organism. The amino acid evolution of cyt-b is generally conservative and is not expected to be affected by significant levels of positive selection for nonsynonymous change in mammalian carnivores, rodents, cetaceans, or artiodactyls (Andrews, Jermiin, and Easteal 1998; Grossman et al. 2001). However, in organisms where the demands of metabolic processes drastically change or shift during cladogenesis, the hypothesis that key functional cyt-b amino acid sites have experienced positive selection may be viable even though the overall evolution of the gene is evolutionarily conservative. Such a shift has been demonstrated among cetacean globins (Naylor and Gerstein 2000) and also is likely to have occurred in cetacean cyt-b proteins.
The mitochondrial cyt-b gene and associated protein have been studied extensively in the past several years. Sequences of cyt-b have been utilized in character sets for phylogenetic reconstruction in a wide variety of organisms, including cetacean groups (e.g., Milinkovitch, Meyer, and Powell 1994; Arnason and Gullberg 1996). The importance of its domain structure and biochemical function has been determined in great detail (Degli Esposti et al. 1993; Zhang et al. 1998; Iwata et al. 1998). The cyt-b protein is composed of three functional domains: the intermembrane domain, which is composed of four loops (designated ab, cd, ef, and gh)the two central loops are much longer than the othersthat extend into the mitochondrial cristae; the transmembrane, which is composed of eight -helices (designated A to H) that traverse the inner mitochondrial membrane from the matrix into the cristae; and the matrix domain, which is composed of the two termini (amino [N] and carboxyl [C]) and three loops (designated bc, de, and fg), all of which extend into the mitochondrial matrix (Irwin, Kocher, and Wilson 1991; Degli Esposti et al. 1993; Zhang et al. 1998). The intermembrane domain has been found to be primarily involved in the creation of a proton gradient and the transfer of electrons to the cytochrome c protein. The transmembrane domain is primarily responsible for anchoring the protein securely within the inner mitochondrial membrane, but it also has been implicated in the creation of the proton gradient, and it provides ligation sites for protein-protein interactions within the cytochrome bc1 complex. The matrix domain, however, has been implicated in very few functional activities, the exception being a few amino acid residues close to the N-terminus that assist in the creation of the proton gradient (Degli Esposti et al. 1993).
Other studies have established a correlation between the rate of nucleotide/amino acid evolution with the structure/function of the domains of cyt-b (Irwin, Kocher, and Wilson 1991; DeWalt et al. 1993; Griffiths 1997; McClellan and McCracken 2001). These studies have found that the transmembrane domain evolves most quickly overall, followed by the matrix domain. The intermembrane domain, however, evolves extremely slowly. The importance of maintaining the function of the Q-cycle mechanism by selection is almost always a predominant consideration in interpreting these differential rates of change.
One model that has been successfully used to determine the sites affected by positive selection in terms of quantitative biochemical properties is that presented by McClellan and McCracken (2001). In their limited study of cetartiodactyl cyt-b, they found that although the intermembrane loops experienced fewer changes than expected overall, there were unexpected changes associated with polar requirement in one intermembrane loop in the combined data, including along the phylogenetic branch basal to all cetaceans. However, this study included only four cetacean species relative to just six amino acid properties, which had been shown to be indicative of purifying selection among other mammal species (Xia and Li 1998). Inclusion of additional biochemical, physical, energetic, and conformational amino acid properties, therefore, may lead to the discovery of the properties, or suites of properties, that may have been involved in the hypothesized molecular adaptation experienced by the cetacean cyt-b protein.
One vital requirement of the McClellan and McCracken (2001) method (MM01) is a well-corroborated phylogenetic tree for both artiodactyls and cetaceans for comparison purposes. The backbones of such trees have been provided by Nikaido, Rooney, and Okada (1999) and Nikaido et al. (2001), respectively. Built using synapomorphic SINE and LINE retroposon insertions, it was conclusively shown that (among other things) cetaceans share sister taxonomic status with the artiodactyl family Hippopotamidae (Nikaido, Rooney, and Okada 1999) and that river dolphins are not monophyletic (Nikaido et al. 2001). The MM01 model uses independent estimations of phylogeny such as these to establish a chronology of observable molecular evolutionary events. The frequency of these events are analyzed to identify (1) amino acid properties that may have radically changed more often than expected by chance (presumably because of selection promoting the occurrence of radical amino acid replacements) and (2) amino acid sites associated with selection, thus, establishing a correlation between the sites of positive selection and the structure and function of the protein. A phylogeny reliably reconstructed from data that are independent of the sequence data being analyzed for selective influences is preferable to avoid any semblance of circularity. The trees constructed using SINE and LINE insertion events, thus, fulfill this preference relative to cetacean and artiodactyl cyt-b data.
Using a comparative approach to implement the MM01 and other models, we seek to evaluate the molecular adaptation of cyt-b and identify those sites in the functional regions of the protein that may have experienced a shift in genic selection strategy. We also evaluate the major differences between cetacean and artiodactyls cyt-b evolution among the different functional domains and pay particular attention to any shifts among amino acid sites or physicochemical properties being affected by positive selection for radical change since the divergence of early cetaceans from their artiodactyl ancestors.
We found shifts in both the amino acid residue loci and physicochemical properties influenced by positive selection in the cetacean cytochrome b protein when compared with that of artiodactyls. We also found shifts in the magnitude of selection influencing the evolution of functional subdomains of the protein. We also correlate these shifts with the known function of many of the amino acid residues (determined via site-directed mutagenesis) and the crystal structures of the subdomains in which they reside. Finally, we evaluate correlations found between the results of the MM01 analysis (which exclusively implements information about changes in physicochemical amino acid properties) and other models (that detect and measure selection in terms of patterns of nucleotide change). The general conclusions of this study are (1) selection models that implement dN/dS ratios are generally not sensitive enough to detect more subtle molecular adaptations; (2) there has been appreciable molecular adaptation in the cetacean cyt-b protein relative to that found in their terrestrial relatives, results that correlate closely to the known function of the protein and its domain structure; and (3) there is a close correlation between the results of the MM01 model and at least one other model that utilizes information other than the dN/dS ratio, which verifies our results and presents evidence that the MM01 model may be successfully applied to detecting specific amino acid property adaptations within even relatively conservative proteins.
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Materials and Methods |
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Statistical significance for each CDM window setting was determined using a Bonferroni correction for multiple comparisons, assuming complete independence of individual comparisons. This approach produces results that are conservative because the sliding window analysis implemented in CDM produces comparisons that are not entirely independent because of window settings overlap. Conservative analytical results, however, are desirable to ensure greater confidence. Therefore, statistical significance of the computational output of the CDM sliding window analysis (in the form of successive log-likelihood ratios) was determined using = 0.005 (99.5% confidence) rather than the more usual
= 0.05 (95.0% confidence).
Identifying Selective Influences
Data were analyzed with the intention of identifying sites historically affected by positive selection using the dN/dS model M3 implemented in MrBayes version 3.0b4 (Ronquist and Huelsenbeck 2003) and models M0, M1, M2, M3, M7, and M8 in the PAML algorithm codeml version 3.13 (Yang 1997). MrBayes selection analysis (Nielsen and Huelsenbeck 2002) was accomplished using a GTR+ +I model for one million tree generations and a 65,100-generation burn-in for cetaceans and a 113,800-generation burn-in for artiodactyls.
The magnitude of changes in physicochemical amino acid properties were evaluated using a modification of the MM01 model (McClellan and McCracken 2001) (These modifications, along with the components of the model that were not modified, are briefly outlined below to establish context.), which measures selective influences based on the magnitude of changes in 31 physicochemical amino acid properties (table 2). Information necessary for this evaluation was generated by the computer package TreeSAAP version 2.2 (Woolley et al. 2003).
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To further test the hypothesis of neutrality, the number of inferred amino acid replacements per magnitude category for a given property is divided by the number of evolutionary pathways assigned to that partition to calculate a proportion of fixed pathways, pi, where
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The average influence of selection on amino acid properties per magnitude category is evaluated using a likelihood estimation of a proportion similar to that calculated in equation 1 but taken across all amino acid properties under consideration, where
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Graphical representations of these test statistics may be interpreted according to figure 1A. Significant positive z-scores indicate that nonsynonymous substitutions of magnitude range i are more frequent than expected by chance and are, thus, influenced by positive selection (these amino acid replacements are being preferred by selection), whereas significant negative z-scores indicate they are less frequent than expected by chance and are influenced by negative or purifying selection. Furthermore, when positive selection is detected in lower, more conservative magnitude ranges (categories 1, 2, or 3) as outlined above, the amino acid property (equations 1, 2, 4, and 5) or properties (equations 6, 7, 8, and 9) are considered to be under a type of stabilizing selection (here defined as selection that tends to maintain the overall biochemistry of the protein, despite a rate of change that exceeds the rate expected under conditions of chance, as in figure 1B). Conversely, when positive selection is detected in greater, more radical magnitude ranges (categories 6, 7, or 8), the amino acid property or properties are considered to be under destabilizing selection (here defined as selection that results in radical structural or functional shifts in local regions of the protein, as in figure 1D). We make the assumption that positive-destabilizing selection represents the unambiguous signature of molecular adaptation because when radical changes are favored by selection, they result in local directional shifts in biochemical function, structure, or both. For such changes to be favored by selection (i.e., for such changes to be more abundant than expected by chance), they must instill an increased level of survival and/or reproductive success in the individuals who possess and propagate them.
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The relationship between MM01 goodness-of-fit scores and categorical z-scores, however, is easily explained. Whereas the GF-score estimates a global goodness-of-fit of the data to random expectations and tests the hypothesis that observed and expected distributions are equal, the z-score, as defined in this study, estimates local goodness-of-fit of the data in a particular magnitude category to random expectations and tests the hypothesis that rates of amino acid replacement per evolutionary pathway within that magnitude category is equal to the overall mean rate. Whereas an individual categorical z-score within a distribution of amino acid property changes may indicate rejection of the null hypothesis locally, the goodness-of-fit for that amino acid property may indicate global acceptance of the null. Thus, a particular magnitude of amino acid change may be favored or disfavored by selection, although the average effect across all magnitudes of change may appear (deceptively) nearly neutral. Therefore, both statistical analyses are necessary for appropriate interpretation of the data.
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Results and Discussion |
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MM01 analysis confirms that positive selection on stabilizing changes coupled with negative selection on destabilizing changes is the predominant mode of selective influence among amino acid replacements (as mentioned above) affecting both cetacean and artiodactyl cyt-b proteins within all three functional domains. The proportion of amino acid properties being influenced by positive selection in conservative magnitude categories 1, 2, and 3 is greater than the proportion of properties influenced by negative selection (frequencies of amino acid properties affected by positive [p] and negative [n] selection in artiodactyls and cetaceans combined: intermembrane, f(p) = 0.328, f(n) = 0.258; matrix, f(p) = 0.333, f(n) = 0.210; and transmembrane, f(p) = 0.355, f(n) = 0.253). The opposite is true (and much more pronounced) for the more radical magnitude categories 6, 7, and 8 (intermembrane, f(p) = 0.054, f(n) = 0.145; matrix, f(p) = 0.097, f(n) = 0.194; and transmembrane, f(p) = 0.065, f(n) = 0.575), especially in the transmembrane domain. Despite this, near neutrality dominates in every functional domain (on average), except the transmembrane (the frequency of amino acid properties that are nearly neutral across all magnitude categories: intermembrane = 0.597; matrix = 0.595; and transmembrane = 0.325). The relationship between positive and negative selection among stabilizing and destabilizing amino acid replacements is also apparent in figure 5, although all but a few magnitude categories resulting from analyses of the artiodactyl and cetacean transmembrane domains are within the statistical zone of near neutrality, as defined by the critical value at = 0.05. Thus, even though several individual amino acid properties have been shown to be influenced by selection, the average effect is of near neutrality. This underscores the importance of appropriately designing analytical experiments meant to describe the mode of molecular evolution such that biologically meaningful information is not obscured by combining conflicting results, such as is the case with these amino acid properties and dN/dS ratios in general.
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The biochemical shift in cyt-b resulting from the transition of cetacean ancestors from a terrestrial to an aquatic habitat is illustrated phylogenetically in figure 2 and graphically in figure 6. The artiodactyl cyt-b intermembrane has historically been influenced by positive-destabilizing selection relative to a suite of properties that includes c (p > 0.999, p7), Ht (p > 0.999, p6), and Pc (p > 0.999, p8). As soon as cetaceans diverged from artiodactyls, this selection regime was replaced by selection for radical change in another suite of properties, including pK' (p > 0.999, p8), and Ra (p = 0.955, p7). Further evidence for cetacean molecular adaptation in this domain is illustrated in figure 7. Of all the amino acid residue loci influenced by positive-destabilizing selection, only three are exclusive to artiodactyls, and only two are shared. The remaining eight amino acid residue sites are under selection exclusively in cetaceans, most of which are closely associated with key functional regions (within helices or immediately adjacent to them) within the domain. Notably, all of the highlighted residue sites in the cd-loop and all but one in the ef-loop in figure 7 are exclusive to cetaceans. Derived characters at these loci underscore the molecular evolutionary shift experienced in this domain and represent, without exception, sites that are spatially associated with either other functional regions in the domain (as is the case with the three helices of the ab-loops and cd-loops) or other proteins in the complex (as are those in the cd1-helix and ef-loop) (Degli Esposti et al. 1993; Zhang et al. 1998; Iwata et al. 1998).
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Finally, in artiodactyls, residues 57 and 247 (both of which are near the transmembrane-intermembrane boundary and are in either direct or close proximity to cytochrome c1 when ISP is in its "c1" state [Iwata et al. 1998]) have been influenced by selection on a single suite of properties: P Pc,
c, Ht, and Pt (no other amino acid sites in the intermembrane domain were affected by positive-destabilizing selection on more than one property [see table 4]). Radical changes in these properties (three of which are conformational properties) at these sites may have constituted minor overall adjustments in the length and relative orientation of membrane-spanning transmembrane subdomains. Combined, these two sites experienced 25 amino acid replacements, all but two of which may have been influenced by positive-destabilizing selection.
Matrix Domain
Amino acid residue sites where positively selected radical changes took place in the matrix domain are illustrated in figure 8. Five of these residue loci are unique to artiodactyls, six are unique to cetaceans, and eight are shared. The five residues highlighted between residues 10 and 20 all are within the -helix, which is located in an area implicated in the proton input function of the protein (Degli Esposti et al. 1993). Three of the five residues are common to both artiodactyls and cetaceans, suggesting that an optimization process of this function has continued uninterrupted from early artiodactyls in both clades. These three residues have been influenced by positive selection for radical change relative to the same amino acid properties in both clades (pK', residue 14; P
, residue 17; and pK', residue 19). Selection on the other two residues, however, appears to be differentially optimizing
c in this local region of the protein.
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In contrast to the intermembrane domain, artiodactyls and cetaceans share all but two positively selected amino acid properties that favor destabilizing changes; P, pK', h,
c, Ra, Ht, and Pt are common to both, but positive-destabilizing selection relative to Hp (p = 0.982, P6) is unique to artiodactyls, whereas F (p = 0.971, p8) is unique to cetaceans. Several residues exhibit evidence for suites of properties being affected simultaneously. However, this characteristic is expressed differentially in artiodactyls and cetaceans as well. Four of the five residues where suites of properties appear to be affected are clustered in the artiodactyl N-terminus (suite h/Ra/Ht/Pt in residue 2; pK'/h/Ra/Ht/Pt in residue 4; h/
c/Pt in residue 11; and
c/Ht in residue 25) and include eight amino acid replacements on five branches (all terminal) of the artiodactyl phylogenetic tree, affecting only five of the 15 taxa.
Transmembrane Domain
A schematic indicating the orientation of the transmembrane domain components to the inner mitochondrial membrane is illustrated in figure 9. In all, 60 residues were found to have been influenced by significant levels of positive-destabilizing selection: three in the A-helix, seven in the B-helix, seven in the C-helix, 10 in the D-helix, nine in the E-helix, five in the F-helix, six in the G-helix, and 13 in the H-helix. Of these, 16 are unique to artiodactyls, 19 are unique to cetaceans, and the remainder (25) are shared by both clades. There are two noteworthy clusters of these residues found to be affected by positive-destabilizing selection: (1) between residues 188 and 200 in the D-helix (within which is the heme-group ligation site H197) and (2) between residues 228 and 244 in the E-helix. Curiously, both of these clusters occupy protein regions that are densely populated with residues that have been found to be involved with the protein's proton-input function (Degli Esposti et al. 1993). Thus, the radical changes in these clusters may be the result of molecular adaptation just as changes associated with proton-input in the de-loop of the matrix domain also may be (as discussed above).
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The selection dynamics of amino acid properties influenced by positive-destabilizing selection in the second cluster (in the E-helix) is much more complex than that of the D-helix. The properties pK' and P are of particular interest because they exhibit differential patterns of radical change in the two taxonomic groups. Unlike the amino acid replacement cluster of the D-helix, the vast majority of the radical amino acid changes in the E-helix cluster are inferred to have taken place on terminal branches in both the cetacean and artiodactyl phylogenetic trees (89% and 91%, respectively). This may be at least somewhat caused by relatively recent fixation of ancestral polymorphisms (lineage sorting).
Spatial Correlation of CDM and MM01 Analytical Results
Although the correlation between the results of the CDM and MM01 analyses are not completely consistent, at nearly every locus where the CDM indicates that there has been selection among nonsynonymous nucleotide substitutions, MM01 indicates that there have been amino acid replacements affected by positive-destabilizing selection hiding among a greater number of stabilizing changes (fig. 10).
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As noted above, cetacean A-helix, D-helix, and E-helix and de-loop fit CDM predictions much better than the artiodactyl homologs. This could be the result of one of two largely mutually exclusive phenomena: relaxed functional constraints or increased purifying selection. The correlation between CDM and MM01 analyses illustrated in figure 10 allows for the differentiation of these two causative influences. The near absence of adaptive changes in the cetacean A-helix is most likely caused by increased purifying selection, resulting in a better fit to random expectations. The cetacean D-helix and E-helix, however, have experienced a much greater number of adaptive amino acid replacements than has the artiodactyl homologs, suggesting that these changes were permitted by relaxed selective constraints. The regions within these subdomains that fail to fit CDM expectations correspond closely to the clusters of residues (residues 188 to 200 and 228 to 244, plus variable residues 245 and 247 in the ef-loop) found to be influenced by positive-destabilizing selection. The lack of total correspondence, however, suggests that variable residues 228, 232, and 234 may not have experienced adaptive change at all (false positives), but are merely random changes that failed to affect the overall pattern of nucleotide substitution in this region. The remaining variable residues experiencing radical amino acid replacements most likely have been historically influenced by positive-destabilizing selection.
The possibility that MM01 may be producing false-positive results is of great concern. Analytical strategies must be derived that will allow for greater confidence in results. However, for the purposes of this study, correspondence between the analytical results and the known structure and function of the active protein provide a great deal of confidence. Furthermore, correlations between MM01 and CDM sliding window results can be considered a sign of even greater confidence.
The results of this study may be considered evidence that a total reliance on dN/dS ratios to detect molecular adaptation will result in a lack of sensitivity for two reasons: (1) molecular adaptation may be the result of even single amino acid changes, resulting in dN/dS < 1.0, and (2) the dominant mode of molecular change may result in the majority of amino acid replacements being stabilizing rather than destabilizing, and, thus, they may not be adaptive, even when dN/dS > 1.0. This is not to say that dN/dS is not useful for detecting positive selection. Our results do not contradict this paradigm. We only suggest that "positive selection" may not always be synonymous with "molecular adaptation." It may be advisable to consider other genetic information content for the purpose of characterizing adaptation; a simple comparison of nonsynonymous and synonymous rates of change may not be adequate. It may be time for a shift in thinking relative to adaptation: Molecular adaptation is a function of changes in protein phenotype resulting from corresponding changes in suites of physicochemical amino acid properties. Such a shift will result in more detailed characterizations of molecular adaptation, such that correlations may be drawn between molecular evolution and studies that focus on the description of protein structure and function.
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Acknowledgements |
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Footnotes |
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2 Present address: Harvard Law School, Cambridge, Massachusetts.
3 Present address: Department Biology and Biomedical Science, Washington University, St. Louis, Missouri.
Michele Vendruscolo, Associate Editor
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