Abstract
Background
The mitochondria produce up to 95% of a eukaryotic cell's energy through oxidative phosphorylation. The proteins involved in this vital process are under high functional constraints. However, metabolic requirements vary across species, potentially modifying selective pressures. We evaluate the adaptive evolution of 12 protein-coding mitochondrial genes in 41 placental mammalian species by assessing amino acid sequence variation and exploring the functional implications of observed variation in secondary and tertiary protein structures.
Results
Wide variation in the properties of amino acids were observed at functionally important regions of cytochrome b in species with more-specialized metabolic requirements (such as adaptation to low energy diet or large body size, such as in elephant, dugong, sloth, and pangolin, and adaptation to unusual oxygen requirements, for example diving in cetaceans, flying in bats, and living at high altitudes in alpacas). Signatures of adaptive variation in the NADH dehydrogenase complex were restricted to the loop regions of the transmembrane units which likely function as protons pumps. Evidence of adaptive variation in the cytochrome c oxidase complex was observed mostly at the interface between the mitochondrial and nuclear-encoded subunits, perhaps evidence of co-evolution. The ATP8 subunit, which has an important role in the assembly of F0, exhibited the highest signal of adaptive variation. ATP6, which has an essential role in rotor performance, showed a high adaptive variation in predicted loop areas.
Conclusion
Our study provides insight into the adaptive evolution of the mtDNA genome in mammals and its implications for the molecular mechanism of oxidative phosphorylation. We present a framework for future experimental characterization of the impact of specific mutations in the function, physiology, and interactions of the mtDNA encoded proteins involved in oxidative phosphorylation.
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Background
Mitochondrial DNA (mtDNA) has long been treated as an ideal marker because of its convenience for reconstruction of gene genealogy and population history inference. However, the selective neutrality assumption of the mtDNA is simplistic because variation in mitochondrial protein-coding genes involved in oxidative phosphorylation (responsible for the production of up to 95% of the energy of eukaryotic cells) can directly influence metabolic performance. Because of the importance of this biochemical pathway, evaluating selective pressures acting on mtDNA proteins could provide key insight into the adaptive evolution of the mtDNA genome as has been suggested by recent empirical evidence (e.g. Ruiz-Pesini et al. 2004 [1], Moyer et al. 2005 [2], Bazin et al. 2006 [3]). As amino acid changes cause inefficiencies in the electron transfer chain system, oxidative phosphorylation produces reactive oxygen molecules, causing oxidative damage to mitochondrial and cellular proteins, lipids and nucleic acids, and eventually interrupting the production of mitochondrial energy.
Mutations in mitochondrial-encoded genes can influence the production of reactive oxygen species in mice [4] and have been implicated in a large number of human and mouse diseases [5]. Some amino acid changes may also improve aerobic capacity and adaptation to new thermal environments [6–11]. Furthermore, mutations in mitochondrial genes have been implicated in exercise intolerance in humans [12] (see Table 1). Metabolic capacity varies widely among mammalian species [13], and variation in the elements of the oxidative phosphorylation pathway have been linked to different life history traits and environmental adaptations [14–16]. In addition, functional interactions between mitochondrial- and nuclear-encoded proteins may result in co-adaptation to maintain or improve metabolic fitness [9, 17].
The mtDNA comprises a closed circular DNA strand that encodes the following proteins involved in the oxidative phosphorylation machinery: seven subunits of the NADH dehydrogenase or NADH ubiquinone oxidoreductase complex (ND: ND1, 2, 3, 4, 4L, 5 and 6), the cytochrome b subunit of the ubiquinol cytochrome c oxidoreductase or cytochrome bc1 complex (CytB), three subunits of the cytochrome c oxidase complex (COX), and two subunits of ATP synthase (ATPase: ATP6 and ATP8) [18] (Figure 1A). These key components of four of the five complexes involved in oxidative phosphorylation, combine with other subunits encoded by the nuclear DNA genome [18] (Figure 1B). In the oxidative phosphorylation, reducing equivalents resulting from the oxidation of nutrients such as glucose, are carried by a series of molecules (the electron transport chain), which have increasing standard reduction potentials. The resulting free energy is transformed into a proton gradient that is used by ATP synthase to produce ATP (adenosine triphosphate) from ADP (adenosine diphosphate) [18].
Although mtDNA sequence data have been extensively used in mammalian phylogenetics [19–24], relatively little attention has been devoted to the study of molecular adaptation of mitochondrial encoded proteins. In this work we combine molecular evolution analyses with crystallographic and secondary structure prediction analyses to explore how mitochondrial genetic variation may be linked to the diverse metabolic patterns of 41 mammalian species (Table 2) from each of the four major clades of the placental mammals. Since most of the cell's ATP production results from mitochondrial oxidative phosphorylation, we also use estimates of metabolic rates based on oxygen (O2) consumption under aerobic conditions as a surrogate for physiological or metabolic differences among the species in our analyses.
Results and Discussion
The mitogenomic phylogenetic reconstruction (Figure 2) obtained with Bayesian inference methods [25, 26] is mostly congruent with previous comprehensive analyses from nuclear and mtDNA genes [27] where mammalian species form four major groups: Laurasiatheria, Euarchontoglires, Xenarthra, and Afrotheria. The superordinal tree is resolved with posterior probabilities greater than 0.99. The relative positioning of the orders is in agreement with that obtained using Maximum Likelihood [see Additional file 1, Fig. S2], supporting the previous topology.
Significant physicochemical amino acid changes among residues in mitochondrial protein coding genes were identified by the algorithm implemented in TreeSAAP [28], which compares the observed distribution of physicochemical changes inferred from a phylogenetic tree with an expected distribution based on the assumption of completely random amino acid replacement expected under the condition of selective neutrality (see details in the Methods section). There are more modifications in the number of radical amino acid property changes in the tips (80%) relative to the interior branches (20%) of the mammalian tree (Figure 3). The interior branches with the highest number of radical amino acid property changes are those that lead to the house mouse and the rat (node 78 to 80; a total of 55 changes across all proteins), the branch leading to the elephant shrews (node 45 to 46; 43 changes across all proteins), the branch leading to the guinea pig and the greater cane rat (node 78 to 79; 21 changes across all proteins), and the branch uniting the humpback whale and white-beaked dolphin (node 64 to 65; 17 changes across all proteins). There was an average of 10 changes per interior branch. The proteins with the highest average number of changing properties per site are ND and ATPase (0.7 and 0.8 average changes per site, respectively), while CytB and COX were the lowest (0.3 and 0.2 changes per site, respectively) (Figure 4). Noteworthy, a distinction arises between loop areas and transmembrane domains for all proteins complexes. Loop areas are, in general, more affected by positive selection than transmembrane domains (0.52 property changes showing strong positive selection per site vs 0.39 for the latter).
Amino acid properties with signals of strong positive selection accumulated at a rate roughly equivalent to the mutation rate of the gene itself (i.e. mutation rate of ATP > ND > CytB > COX; see Lopez et al. 1997 [29]) (Figure 5; [see Additional file 1, Fig. S3]). This correlation is more pronounced for the protein-coding genes with higher mutation rates, such as ATPase and ND, as is most apparent in analyses of the variation along the interior branches, where 20% of the radical amino acid property changes occur [see Additional file 1, Fig. S3]. The best correlation between overall mutation rate and number of sites with radical amino acid changes was observed for NDs, while the existence of several outliers for ATPase slightly reduced the strength of the correlation [see Additional file 1, Fig. S3].
The biochemical complexity of the oxidative phosphorylation processes precludes a clear discussion on the functional implications of the amino acid properties that are under selection (Figures 6 and 7). Negative selection dominates the categories of radical changes. Moderate changes (categories 1 and 2) characterize most of the positive selection detected. Properties under positive destabilizing selection (the power to be at the N-terminal, refractive index, long-range non-bonded energy, coil tendencies, compressibility, turn tendencies and the power to be at the C-terminal) will interfere both at a chemical and structural level. However, since we are considering events as diverse as protein-protein interactions, molecular oxygen and proton diffusion and electron-transfer, it is not possible to establish a direct correlation with the previously referred properties. Discussion on varying amino acid properties will therefore be made specifically for those sites which can be mapped on available structural data.
NADH dehydrogenase
The sites with the highest number (between 17 and 18) of strong positively selected changes in amino acid properties are within ND2, ND4 and ND5 [see Additional file 1, Fig. S4]. These subunits show a high average in the number of such changes per residue (1.1, 0.6 and 0.8, respectively, for an overall average of 0.5) (Figure 4). NADH dehydrogenase is the first (Figure 1B) and the largest enzyme complex in the respiratory chain. It receives electrons from the oxidation of NADH and provides electrons for reduction of quinone to quinol. This is coupled to the translocation of four protons across the inner membrane, generating an electrochemical proton gradient (Figure 1B). Complex I is an L-shaped complex (as shown in low resolution electron microscopy analysis [30]) that contains all seven mtDNA encoded subunits in a membrane-embedded arm. The subunits of the "peripheral arm" (that projects into the mitochondrial matrix) are encoded by the nuclear DNA genome. Forty-six different units have been identified in complex I from bovine heart mitochondria [31], although the 14 bacterial subunits are the minimum needed for sufficient energy transduction by complex I [32]. The electron transfer chain events occur in the peripheral arm subunits and the proton pumping occurs in the membrane-embedded module. ND1 and ND2 have been suggested to be located at the junction between the peripheral and the membrane arm [32], while ND4 and ND5 should occur at the distal end of the latter [31]. ND2, ND4, and ND5 are suggested to be the actual proton pumping devices because of their sequence homology with a class of Na+/H+ antiporters [32]. The overall variation in these subunits, which have already been assigned some function, is larger than the observed variation in the subunits with still unknown functions. Mutations in these subunits may interfere with the efficiency of the proton-pumping process. This could either occur through chemical changes that hinder/improve the proton translocation or by disrupting/improving the long-range redox-linked conformational changes that are suggested to occur in order to, for example, activate ND5 which is far away from the electron transfer events [32]. The number of TM domains predicted in this study (Figure 8) for ND2, ND4, and ND5 is similar to that present in their bacterial counterparts (which are 11, 12, and 17, respectively) [33]. Figure 8 shows that the sites with higher variation (20 < number of changes < 110) are located only in the loop regions, suggesting that functional constraints are acting upon the TM domains, which would be consistent with their putative proton-pumping function.
Cytochrome b
CytB is an extremely conserved protein, reflecting its fundamental role in energy production in the mitochondria. It catalyses reversible electron transfer from ubiquinol to cytochrome c coupled to proton translocation (Q-cycle [34, 35]). A quinol molecule at the Q0 site donates an electron to cytochrome c via the iron-sulfur protein (ISP) and cytochrome c1 (Figure 9). A second electron passes sequentially through the bL and bH heme ending up in a quinone/semiquinone radical at the Qi site. In a complete Q cycle, two quinol molecules are the Q0 site and one molecule of quinol is regenerated while four protons are translocated across the membrane. Available x-ray data showing cytochrome bc1 inhibitors bound to both Qi and Q0 sites suggest that these mutations have functional consequences. The high degree of conservation among CytB sequences (Figure 4) made it difficult to find evidence of positive selection using TreeSAAP. Sites detected by TreeSAAP were mainly located at the interface between mitochondrial and nuclear-encoded subunits (Figure 9). However, insight into the evolution of this protein was obtained by inspection of the amino acid substitutions observed in the sequence alignment of the various mammalian species.
The African savanna elephant Loxodonta Africana has two atypical amino acid replacements at positions 16 and 260 (Figure 10) that are conserved in other members of the Elephantidae family (the African forest elephant Loxodonta cyclotis, the Asian elephant Elephas maximus, and both the extinct woolly mammoth Mammuthus primigenius and the American mastodon Mammut americanum; [see Additional file 1, Fig. S5]). Another uncommon amino acid occurs in most of the Elephantidae group at position 266, but not in the American mastodon, suggesting that this mutation originated less than 24 million years ago, after the divergence of the American mastodon from the other four proboscidean species analyzed [36] [see Additional file 1, Fig. S5]). At the N-terminal site 16, adjacent to the Qi pocket, there is a positively charged residue (lysine) that will affect the binding of ligands at this site (Figure 9). Residues 260 and 266 are located in loop ef, which contributes to the formation of the ISP binding crater. Changes in shape complementarity between the ISP and CytB have been shown to be important for the control mechanism of ISP conformational change, an important feature of cytochrome bc1 mechanism [37]. The aspartate on position 260, a conserved asparagine spot (Figure 10), may affect both the interaction with the ISP (protein recognition event) and other electron transfer events, as it adds an extra negative charge to this interface (Figure 9). Also on loop ef, the shift from the conserved proline on site 266 to a leucine will alter the rigidity of the secondary structure, with implications in the protein-protein interface contacts (Figure 9).
The cetaceans have an alanine in position 266 (Figure 10), which will have a similar consequence to that referred above. The biochemistry involved in aquatic mammals is still a matter of debate, but it has been found that mitochondria of seals can survive ischemia for much longer than terrestrial mammals [38], suggesting changes in function or regulation. The humpback whale has another interesting mutation. An arginine in site 110 (Figure 10) changes the local net charge, and being located just outside the coordination sphere of heme bH, such a difference can interfere with the electron transfer events (Figure 9). Such alteration is also seen in two of the Chiroptera species, the Ryukyu flying fox and the New Zealand long-tailed bat (Figure 11). Curiously, the sloth, one of the mammals showing a lower metabolic rate than expected given its body size [14], has a positively charged residue on site 111 (a lysine instead of the highly conserved glutamate), a yet more radical change in net electric charge at this location (Figure 10).
Another region that shows radical amino acid variation is helix cd2, close to the hinge region of ISP (Figure 9). Mutations in the hinge region were shown to have drastic consequences in the catalytic activity of ISP, by hindering the conformational changes that are required for cytochrome bc1 function [39]. Two species have a proline residue on site 158: the greater cane rat, a rodent with spiny fur on the back, and the pangolin, the scaly-anteater, which has a low metabolic rate because of the combination of an invertebrate diet and a large body size [14]. Sites 16, 159, 162 and 263 show an elevated number of amino acid changes (Figure 10), suggestive of adaptive relevance in many mammalian species. Site 277 presents a highly conserved alanine residue. It is placed in the middle of helix F1, within the Q0 pocket (Figure 9). Two peculiar species (dugong and alpaca) with distinct metabolic requirements show very radical amino acid changes at this site. The dugong is an aquatic mammal that is more closely related to elephants than to other marine mammals [40]. It is sometimes referred to as a sea cow because of its strict sea-grass diet, combining several interesting features from the metabolic point of view: a large body size, a low energy diet and aquatic environment adaptation. It has an arginine residue in position 277, which will not only cause extra steric hindrance because of the size of the side chain relative to alanine (in Figure 9 the van der Waals surface of an arginine side chain is presented; it clearly overlaps the stigmatellin binding position), but will also change the binding mode of the ligand, as it is positively charged. Finally, an important change was detected in the alpaca, a domesticated breed of South American camel-like ungulates that lives at an altitude of 3500 to 5000 meters above sea-level presenting metabolic adaptations to the low O2 environment [41]. This species has a proline at site 277, which will drastically alter the local secondary structure, disrupting the alpha helix and therefore changing the shape of the Q0 pocket. Such a mutation is not present in the closest relatives of the alpaca [see Additional file 1, Fig. S5]. Curiously, the Old World members of the Camelidae family (the dromedary Camelus dromedarius and the bactrian camel C. bactrianus) have an aspartate in position 16, instead of the asparagine exhibited by the four species that inhabit South America (alpaca Lama pacos, guanaco L. guanicoe, llama L. glama, and vicuna Vicugna vicugna) [see Additional file 1, Fig. S5]. Several mutations in human CytB have been related to exercise intolerance [12] (Table 1; Figure 9), all of which have similar chemical effects including a change in the net charge around the heme groups and in the binding pockets and disruption of local secondary structures close to the substrate binding areas.
Cytochrome c Oxidase
Complex IV is the terminal electron transfer chain complex that catalyses the electron oxidations of four consecutive reduced cytochrome c molecules and the concomitant reduction of one O2 molecule to water [42]. COXI and II subunits are directly involved in the electron transfer and proton translocation processes, while the other 11 subunits are thought to have regulatory roles. Results obtained with TreeSAAP were mapped on an available 3D structure for COX (Figure 11). COXI and II are the most conserved of all the 12 subunits analyzed (Figure 3; Figure 4; [see Additional file 1, Fig. S4]). Recent studies on taxa that had clearly experienced significant changes in their metabolic needs detected positive selection acting in COXI and COXII, namely in carnivorous plants [7] and in high-performance fish [6]. As demonstrated in high-performance fish COXII subunits, the highly variable sites in mammals are located at the interface between mitochondrial and nuclear-encoded subunits (Table 3), suggesting either an unknown biological role or the occurrence of compensatory mutations in the nuclear subunits (co-evolution). The known functional spots in the complex were found to be very conserved, namely the portion of COXII directly involved in the electron transfer from cytochrome c to the binuclear CuA center (hydrophobic loop consisting of His102-Tyr105 depicted in Figure 11), the negative patch that surrounds it (Asp119, Glu132, Asp139, Glu157, Asp158) [43], the metal binding sites, and the proposed proton/O2/water pathways [44–46]. COXIII had more variable sites, which is expected due to the fact that it has no associated redox cofactors. Also, COXIII has been shown to be unnecessary in some bacterial organisms [45]. Some other interesting changes in COX included the K216E mutation (charge inversion) in COXII observed only in cetaceans, and Y182H in COXIII only seen in the hippopotamus and the dugong (Figure 11). Site 185 in COXIII shows a big variation in amino acid type across all species. Finally, the mutation M29K in COXII has been shown to cause exercise intolerance in humans (Table 1; Figure 11).
ATP synthase
The proton-gradient that results from H+ pumping into the intermembrane space, is used by ATPase to synthesize ATP. The proton channel is located in the membrane sector (F0) which is connected to the catalytic component (F1), located on the matrix side of the membrane (Figure 12A). The latter, when separated from the membrane, behaves as a soluble ATPase. Large cooperative conformational changes occur in order to couple the passage of protons through the membrane arm and the production of ATP [47, 48]. In the proposed mechanism for E. coli ATPase, protons that have accumulated in the periplasm enter the assembly via subunit a (corresponding to ATP6 in yeast [49]). One proton binds between two c subunits (corresponding to ATP9 subunits in yeast [49]). In order for the proton to reach the exit channel, the c subunits (in a total of 10 in E. coli), that are arranged as a cylinder, have to rotate, releasing the proton after 10 steps of proton binding. This rotation movement involves the γ and ε subunits, that remain fixed to the top of one set of c subunits. The rotation of γ within the α/β subunits induces conformational changes that release ATP from the alternating catalytic cycles. The ε subunit (homologous to mitochondrial subunit δ and IF1 regulatory protein) is responsible for determining whether complex V acts as a synthase or catalyses the reverse reaction (pumping protons from the cytoplasm/matrix to the periplasm/intermembrane space) at the expenses of ATP hydrolysis. Subunits b and δ (equivalent to mitochondrial OSCP) keep the α/β subunits in a fixed position. The conservation of ATP6 reflects its key role in the coupling of the proton flow with the rotation of the c subunits: as for the ND complex, the sites with higher variation are located only in the predicted loop regions (Figure 12B).
The ATP8 gene encodes a core subunit of the F0 component of ATPase. In its absence, the ATPase in yeast contains no ATP6 subunit, which suggests an important role in the assembly of F0 [50]. Nevertheless, ATP8 subunit has some highly variable sites (Figure 12B), presenting the higher average of radically changing amino acid properties per residue, suggesting some variation of its regulatory role across species.
Oxidative phosphorylation vs metabolic rates
Some mutations in mitochondrial genes are responsible for severe phenotypic effects related to metabolic capacity, such as exercise intolerance in humans [11] (see Table 1). However, the scaling of metabolic rates in relation to the ATP yield through oxidative phosphorylation is not straightforward, as the total metabolic rate is influenced by multiple characteristics varying across species. The scaling of metabolic rates by body size has been discussed for more than a century [13] and remains controversial [13, 51–54], although generally metabolic rate increases as body mass increases (see this trend in our dataset: Figure 13 and Table 2). However, there are numerous exceptions such as for large tropical ant and termite predators (e.g. the sloth, the aardvark, some pangolins, tamanduas and armadillos), for which basal rate of metabolism decreases when their size increases [14]. Furthermore, variation in size is accompanied by changes in the metabolic rates inherent to cells (in vitro studies showed that these decline with increasing body mass; [55]). Some of the approaches to scale the whole body basal metabolic rate take into account the scaling of individual organ masses and metabolic rates [56]. However, variation can occur in the activity of oxidative enzymes, such as in the case of acclimatization to altitude by lamas and alpacas [41].
The variation between the metabolic rates in different species is a consequence of multiple factors, including the need to maintain body temperature, the number of mitochondria and the volume densities and/or cristae surface, and the fact that relative organ mass and organ metabolite rate varies interspecifically. For example, in reptiles, the lower metabolic rates, compared to mammals, are due to a combination of smaller internal organs, lower mitochondrial volume and cristae surface densities [55].
The scaling of metabolic rates is thus an intricate issue, and even recent multiple-cause models [52] are flawed [57]. Adding to the complexity of interspecies metabolic rates analysis is the random accumulation of variation in the coding sequences of proteins directly involved in energy production and differential selective pressures that arise as mutations affecting mitochondrial ATP production.
Conclusion
We present a mammalian phylogeny based on variation in protein-coding mtDNA genes among 41 representative species. Sequence analyses were complemented with functional analyses to assess the potential importance of mutations leading to radical changes in the physicochemical properties of the amino acids. Most of the mtDNA protein-coding genes were extremely conserved, reflecting their vital role in oxidative phosphorylation. However, much of the observed variation had plausible adaptive significance.
The ND2, ND4, and ND5 complex I genes showed higher than average adaptive variation, with all of the variable sites located in the assessed loop regions of these putative protons pumps (3D structural data are needed to further confirm these interpretations and to measure the functional implications).
The available high resolution 3D structure of CytB facilitated interpretation of the functional implications of mutations occurring at portions of the protein which resulted in extreme amino acid properties variation in species with peculiar metabolic requirements (such as adaptation to low energy diet vs large body size, namely in elephant, dugong, sloth, and pangolin; and adaptation to extreme O2 requirements, i.e. diving in cetaceans, flying in bats, and high altitudes resistance in alpacas). The adaptive variation in COX was restricted mostly to the interface between mitochondrial and nuclear-encoded subunits, suggesting either co-evolution or some influence in the regulatory role of the latter. Among the ATPase subunits, ATP8 which has an important role in the assembly of F0, showed the highest amount of adaptive variation in this analysis. ATP6, which has an essential role in the ATPase rotor performance, showed a high adaptive variation in predicted loop areas. Interpretation of possible functional roles of these changes is limited, however, by the lack of experimental and structural data for these genes.
Our study provides insight into the adaptive evolution of the mtDNA genome in mammals, which may have facilitated the successful radiation and diversification of mammalian species into different environments and habits. The evidence of positive selection acting in important functional regions of the various mammalian mtDNA proteins provides the framework for future experimental characterization of the impact of specific mutations in the function, physiology, and interactions of the mtDNA encoded proteins involved in the oxidative phosphorylation.
Methods
Phylogenetic analyses
A mammalian mitogenomic phylogeny was constructed using 12 of the 13 protein-coding genes of the mtDNA genome of 41 species representative of all mammalian orders (Table 2). The ND6 gene was excluded because it is encoded by the light-strand which has a significantly different base composition from the heavy-chain [58]. Gaps and ambiguous sites adjacent to gaps were removed, resulting in a total alignment of 10,587 nucleotides (3,529 amino acids). The third codon position was excluded from the phylogenetic analysis (7,058 nucleotides were used) because of observed nucleotide saturation [see Additional file 1, Fig. S1].
Bayesian inference methods with Markov chain Monte Carlo (MCMC) sampling were used in MrBayes [25, 26] to assess phylogenetic relationships among the species. We used a General-Time-Reversible substitution model [59] with the invariant site plus gamma options (five categories) after determining the optimal model of sequence substitution with MrModeltest 2.2 [60]. One cold and four incrementally heated chains were run for 2,000,000 generations with chains I = 2, 3, 4, and 5 incrementally heated with heat being 1/(1+ [i-1]T) and T = 0.2. Trees were sampled every 100 generations from the last 1,000,000 generated (well after the chain reached stationarity) and 10,000 trees were used for inferring Bayesian posterior probability. The burn-in fraction performance was evaluated using the program Tracer v1.4 http://tree.bio.ed.ac.uk/software/tracer/. Bayesian methods have been successfully applied to estimation of the tree topology of placental animals using both mitochondrial and nuclear data [27]. A maximum likelihood phylogenetic tree was constructed in PAUP 4.0b10 [61] after determining the optimal model of sequence substitution (TVM+I+G) with Modeltest 3.04 [62].
Adaptive evolution analyses
Selection in protein-coding genes is generally assessed by estimating ω, the ratio between nonsynonymous and synonymous substitution rates (dN/dS) [63]. However, this statistical approach for detecting molecular adaptation is largely biased against even moderately conservative proteins as it does not allow the possibility that adaptation may come in the form of very few amino acid changes. Thus, significant physicochemical amino acid changes among residues in mitochondrial protein coding genes were identified by the algorithm implemented in TreeSAAP [28], which compares the observed distribution of physicochemical changes inferred from a phylogenetic tree with an expected distribution based on the assumption of completely random amino acid replacement expected under the condition of selective neutrality. The evaluation of the magnitude of property change at nonsynonymous residues and their location on a protein 3D-strcuture may provide important insight into the structural and functional consequences of the substitutions [64]. Eight magnitude categories (1 to 8) represent one-step nucleotide changes in a codon and rank the correspondent variation in a property scale of the coded amino acid. Categories 1 to 3 indicate small variation in the amino acid characteristics while categories 6 to 8 represent the most radical substitutions. By accounting for the property changes across the data set, a set of relative frequencies changes for each category is obtained allowing to test the null hypothesis under the assumption of neutral conditions [65]. The categories for which the observed numbers of amino acid replacements in the data set is significantly different from the null model (z-scores > 1.645; P < 0.05) are considered as being potentially affected by selective pressures [65]. Here we focus on amino acid differences that correspond to radical physicochemical variation (positive-destabilizing selection) and are expected to be linked with significant changes in function. TreeSAAP categorizes each amino acid site by positive and negatively destabilizing using 31 properties (henceforth amino acid positions will be referred as sites). To detect strong directional selective pressure, only changes corresponding to categories 7 and 8 (the 2 most radical property changes categories) at the P ≤ 0.001 level were considered. The total number of changes per site is the sum of those occurring in each branch of the phylogeny. The number of changes in amino acid properties was standardized relatively to the overall size of the protein when comparing different complexes (weight factor = total number of amino acids in the complex/total number of amino acids in ATPase, which is the smallest protein complex).
Protein structure analyses
The functional relevance of the amino acid mutations was discussed in the context of existing three-dimensional (3D) structures of mtDNA encoded proteins (CytB [PDB:1PPJ] [66]; COX [PDB:1V54] [67]). For those proteins with unknown 3D structures (ND and ATPase), topologies for transmembrane (TM) subunits were predicted using hidden Markov model (HMM) based servers for topology prediction of transmembrane proteins [68–71]. The algorithm used by the program PRODIV-TMHMM [70] has proven to be very reliable at predicting 3D topologies as it incorporates evolutionary information from multiple sequence alignments and assigns amino acid residues to different TM regions according to their properties. However, since even homologous sequences from the same protein family can have inverted topologies [72], some caution is necessary when using topologies predicted by these automated approaches. We have therefore delineated putative TM domains by integrating the results from PRODIV-TMHMM with two other reliable HMM based methods [73], TMHMM [68, 69] and HMMTOP [71]. Graphic representations of the 3D structures were created with the program VMD [74].
Abbreviations
- mtDNA:
-
mitochondrial DNA
- ATP:
-
adenosine triphosphate
- ADP:
-
adenosine diphosphate
- NADH:
-
nicotinamide adenine dinucleotide
- ND:
-
NADH dehydrogenase
- CytB:
-
cytochrome b
- COX:
-
cytochrome c oxidase complex
- ATPase:
-
ATP synthase.
References
Ruiz-Pesini E, Mishmar D, Brandon M, Procaccio V, Wallace DC: Effects of Purifying and Adaptive Selection on Regional Variation in Human mtDNA. Science. 2004, 303 (5655): 223-226.
Moyer GR, Winemeller KO, McPhee MV, Turner TF: Historical demography, selection, and coalescence of mitochondrial and nuclear genes in Prochilodus species of northern South America. Evolution. 2005, 59 (3): 599-610.
Bazin E, Glemin S, Galtier N: Population Size Does Not Influence Mitochondrial Genetic Diversity in Animals. Science. 2006, 312 (5773): 570-572.
Moreno-Loshuertos R, Acin-Perez R, Fernandez-Silva P, Movilla N, Perez-Martos A, Rodriguez de Cord S, Gallardo ME, Enriquez JA: Differences in reactive oxygen species production explain the phenotypes associated with common mouse mitochondrial DNA variants. Nat Genet. 2006, 38 (11): 1261-1268.
Wallace DC: Mitochondrial Diseases in Man and Mouse. Science. 1999, 283 (5407): 1482-1488.
Dalziel AC, Moyes CD, Fredriksson E, Lougheed SC: Molecular evolution of cytochrome c oxidase in high-performance fish (teleostei: Scombroidei). J Mol Evol. 2006, 62 (3): 319-331.
Jobson RW, Nielsen R, Laakkonen L, Wikstrom M, Albert VA: Adaptive evolution of cytochrome c oxidase: Infrastructure for a carnivorous plant radiation. PNAS. 2004, 101 (52): 18064-18068.
Grossman LI, Wildman DE, Schmidt TR, Goodman M: Accelerated evolution of the electron transport chain in anthropoid primates. Trends Genet. 2004, 20 (11): 578-585.
Blier PU, Dufresne F, Burton RS: Natural selection and the evolution of mtDNA-encoded peptides: evidence for intergenomic co-adaptation. Trends Genet. 2001, 17 (7): 400-406.
Ballard JW, Whitlock MC: The incomplete natural history of mitochondria. Mol Ecol. 2004, 13 (4): 729-744.
Mishmar D, Ruiz-Pesini E, Golik P, Macaulay V, Clark AG, Hosseini S, Brandon M, Easley K, Chen E, Brown MD, Sukernik RI, Olckers A, Wallace DC: Natural selection shaped regional mtDNA variation in humans. PNAS. 2003, 100 (1): 171-176.
Rankinen T, Bray MS, Hagberg JM, Perusse L, Roth SM, Wolfarth B, Bouchard C: The human gene map for performance and health-related fitness phenotypes: the 2005 update. Med Sci Sports Exerc. 2006, 38 (11): 1863-1888.
Suarez RK, Darveau CA, Childress JJ: Metabolic scaling: a many-splendoured thing. Comp Biochem Physiol B Biochem Mol Biol. 2004, 139 (3): 531-541.
McNab BK: Energy constraints on carnivore diet. Nature. 2000, 407 (6804): 584-
Arends A, McNab BK: The comparative energetics of 'caviomorph' rodents. Comp Biochem Physiol A Mol Integr Physiol. 2001, 130 (1): 105-122.
Bonaccorso FJ, McNab BK: Standard energetics of leaf-nosed bats (Hipposideridae): its relationship to intermittent- and protracted-foraging tactics in bats and birds. J Comp Physiol [B]. 2003, 173 (1): 43-53.
Rand DM, Haney RA, Fry AJ: Cytonuclear coevolution: the genomics of cooperation. Trends Ecol Evol. 2004, 19 (12): 645-653.
Saraste M: Oxidative Phosphorylation at the fin de siècle. Science. 1999, 283 (5407): 1488-1493.
Lin YH, Waddell PJ, Penny D: Pika and vole mitochondrial genomes increase support for both rodent monophyly and glires. Gene. 2002, 294 (1-2): 119-129.
Reyes A, Gissi C, Catzeflis F, Nevo E, Pesole G, Saccone C: Congruent Mammalian Trees from Mitochondrial and Nuclear Genes Using Bayesian Methods. Mol Biol Evol. 2004, 21 (2): 397-403.
Phillips MJ, Penny D: The root of the mammalian tree inferred from whole mitochondrial genomes. Mol Phylogenet Evol. 2003, 28 (2): 171-185.
Arnason U, Adegoke JA, Bodin K, Born EW, Esa YB, Gullberg A, Nilsson M, Short RV, Xu X, Janke A: Mammalian mitogenomic relationships and the root of the eutherian tree. PNAS. 2002, 99 (12): 8151-8156.
Murata Y, Nikaido M, Sasaki T, Cao Y, Fukumoto Y, Hasegawa M, Okada N: Afrotherian phylogeny as inferred from complete mitochondrial genomes. Mol Phylogenet Evol. 2003, 28 (2): 253-260.
Broughton RE, Milam JE, Roe BA: The Complete Sequence of the Zebrafish (Danio rerio) Mitochondrial Genome and Evolutionary Patterns in Vertebrate Mitochondrial DNA. Genome Res. 2001, 11 (11): 1958-1967.
Huelsenbeck JP, Ronquist F, Nielsen R, Bollback JP: Bayesian Inference of Phylogeny and Its Impact on Evolutionary Biology. Science. 2001, 294 (5550): 2310-2314.
Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003, 19 (12): 1572-1574.
Murphy WJ, Eizirik E, O'Brien SJ, Madsen O, Scally M, Douady CJ, Teeling E, Ryder OA, Stanhope MJ, de Jong WW, Springer MS: Resolution of the Early Placental Mammal Radiation Using Bayesian Phylogenetics. Science. 2001, 294 (5550): 2348-2351.
Woolley S, Johnson J, Smith MJ, Crandall KA, McClellan DA: TreeSAAP: Selection on Amino Acid Properties using phylogenetic trees. Bioinformatics. 2003, 19 (5): 671-672.
Lopez JV, Culver M, Stephens JC, Johnson WE, O'Brien SJ: Rates of nuclear and cytoplasmic mitochondrial DNA sequence divergence in mammals. Mol Biol Evol. 1997, 14 (3): 277-286.
Radermacher M, Ruiz T, Clason T, Benjamin S, Brandt U, Zickermann V: The three-dimensional structure of complex I from Yarrowia lipolytica: a highly dynamic enzyme. J Struct Biol. 2006, 154 (3): 269-279.
Sazanov LA, Walker JE: Cryo-electron crystallography of two sub-complexes of bovine complex I reveals the relationship between the membrane and peripheral arms. J Mol Biol. 2000, 302 (2): 455-464.
Brandt U: Energy converting NADH:quinone oxidoreductase (complex I). Annu Rev Biochem. 2006, 75: 69-92.
Mathiesen C, Hagerhall C: Transmembrane topology of the NuoL, M and N subunits of NADH:quinone oxidoreductase and their homologues among membrane-bound hydrogenases and bona fide antiporters. Biochim Biophys Acta. 2002, 1556 (2-3): 121-132.
Trumpower BL: The protonmotive Q cycle. Energy transduction by coupling of proton translocation to electron transfer by the cytochrome bc1 complex. J Biol Chem. 1990, 265 (20): 11409-11412.
Mitchell P: Possible molecular mechanisms of the protonmotive function of cytochrome systems. J Theor Biol. 1976, 62 (2): 327-367.
Rohland N, Malaspinas AS, Pollack JL, Slatkin M, Matheus P, Hofreiter M: Proboscidean Mitogenomics: Chronology and Mode of Elephant Evolution Using Mastodon as Outgroup. PLoS Biol. 2007, 5 (8): e207-
Esser L, Gong X, Yang S, Yu L, Yu CA, Xia D: Surface-modulated motion switch: Capture and release of iron-sulfur protein in the cytochrome bc1 complex. PNAS. 2006, 103 (35): 13045-13050.
Castellini MA, Castellini JM: Defining the limits of diving biochemistry in marine mammals. Comp Biochem Physiol B Biochem Mol Biol. 2004, 139 (3): 509-518.
Darrouzet E, Valkova-Valchanova M, Moser CC, Dutton PL, Daldal F: Uncovering the [2Fe2S] domain movement in cytochrome bc1 and its implications for energy conversion. PNAS. 2000, 97 (9): 4567-4572.
Shoshani J: Understanding proboscidean evolution: a formidable task. Trends in Ecology & Evolution. 1998, 13 (12): 480-487.
Hochachka PW, Stanley C, Merkt J, Sumar-Kalinowski J: Metabolic meaning of elevated levels of oxidative enxymes in high altitude adapted animals: an interpretive hypotheses. Respiration Physiology. 1982, 52: 303-313.
Belevich I, Verkhovsky MI, Wikstrom M: Proton-coupled electron transfer drives the proton pump of cytochrome c oxidase. Nature. 2006, 440 (7085): 829-832.
Overholtzer MH, Yakowec PS, Cameron V: The Effect of Amino Acid Substitutions in the Conserved Aromatic Region of Subunit II of Cytochrome c Oxidase in Saccharomyces cerevisiae. J Biol Chem. 1996, 271 (13): 7719-7724.
Hofacker I, Schulten K: Oxygen and proton pathways in cytochrome c oxidase. Proteins. 1998, 30 (1): 100-107.
Richter OM, Ludwig B: Cytochrome c oxidase--structure, function, and physiology of a redox-driven molecular machine. Rev Physiol Biochem Pharmacol. 2003, 147: 47-74.
Schmidt B, McCracken J, Ferguson-Miller S: A discrete water exit pathway in the membrane protein cytochrome c oxidase. PNAS. 2003, 100 (26): 15539-15542.
Fillingame RH, Angevine CM, Dmitriev OY: Mechanics of coupling proton movements to c-ring rotation in ATP synthase. FEBS Lett. 2003, 555 (1): 29-34.
Leyva JA, Bianchet MA, Amzel LM: Understanding ATP synthesis: structure and mechanism of the F1-ATPase (Review). Mol Membr Biol. 2003, 20 (1): 27-33.
Mueller DM: Partial assembly of the yeast mitochondrial ATP synthase. J Bioenerg Biomembr. 2000, 32 (4): 391-400.
Hadikusumo RG, Meltzer S, Choo WM, Jean-Francois MJ, Linnane AW, Marzuki S: The definition of mitochondrial H+ ATPase assembly defects in mit- mutants of Saccharomyces cerevisiae with a monoclonal antibody to the enzyme complex as an assembly probe. Biochim Biophys Acta. 1988, 933 (1): 212-222.
Weibel ER: Physiology: the pitfalls of power laws. Nature. 2002, 417 (6885): 131-132.
Darveau CA, Suarez RK, Andrews RD, Hochachka PW: Allometric cascade as a unifying principle of body mass effects on metabolism. Nature. 2002, 417 (6885): 166-170.
White CR, Seymour RS: Mammalian basal metabolic rate is proportional to body mass2/3. PNAS. 2003, 100 (7): 4046-4049.
West GB, Woodruff WH, Brown JH: Allometric scaling of metabolic rate from molecules and mitochondria to cells and mammals. PNAS. 2002, 99 (90001): 2473-2478.
Else PL, Hulbert AJ: An allometric comparison of the mitochondria of mammalian and reptilian tissues: the implications for the evolution of endothermy. J Comp Physiol [B]. 1985, 156 (1): 3-11.
Wang ZM, O'Connor TP, Heshka S, Heymsfield SB: The Reconstruction of Kleiber's Law at the Organ-Tissue Level. J Nutr. 2001, 131 (11): 2967-2970.
West GB, Savage VM, Gillooly J, Enquist BJ, Woodruff WH, Brown JH: Physiology: Why does metabolic rate scale with body size?. Nature. 2003, 421 (6924): 713; discussion 714-
Gibson A, Gowri-Shankar V, Higgs PG, Rattray M: A Comprehensive Analysis of Mammalian Mitochondrial Genome Base Composition and Improved Phylogenetic Methods. Mol Biol Evol. 2005, 22 (2): 251-264.
Saccone C, Lanave C, Pesole G, Preparata G: Influence of base composition on quantitative estimates of gene evolution. Methods Enzymol. 1990, 183: 570-583.
Nylander JAA: MrModeltest v2. 2004, Evolutionary Biology Centre, Uppsala University
Swofford DL: PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. 1998, Sunderland, Massachussetts , Sinauer Associates
Posada D, Crandall KA: MODELTEST: testing the model of DNA substitution. 1998, 14 (9): 817-818.
Yang Z: Likelihood ratio tests for detecting positive selection and application to primate lysozyme evolution. Mol Biol Evol. 1998, 15 (5): 568-573.
McClellan DA, Palfreyman EJ, Smith MJ, Moss JL, Christensen RG, Sailsbery JK: Physicochemical Evolution and Molecular Adaptation of the Cetacean and Artiodactyl Cytochrome b Proteins. Mol Biol Evol. 2005, 22 (3): 437-455.
McClellan DA, McCracken KG: Estimating the Influence of Selection on the Variable Amino Acid Sites of the Cytochrome b Protein Functional Domains. 2001, 18 (6): 917-925.
Huang LS, Cobessi D, Tung EY, Berry EA: Binding of the respiratory chain inhibitor antimycin to the mitochondrial bc1 complex: a new crystal structure reveals an altered intramolecular hydrogen-bonding pattern. J Mol Biol. 2005, 351 (3): 573-597.
Tsukihara T, Shimokata K, Katayama Y, Shimada H, Muramoto K, Aoyama H, Mochizuki M, Shinzawa-Itoh K, Yamashita E, Yao M, Ishimura Y, Yoshikawa S: The low-spin heme of cytochrome c oxidase as the driving element of the proton-pumping process. PNAS. 2003, 100 (26): 15304-15309.
Krogh A, Larsson B, von Heijne G, Sonnhammer EL: Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001, 305 (3): 567-580.
Sonnhammer EL, von Heijne G, Krogh A: A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol. 1998, 6: 175-182.
Viklund H, Elofsson A: Best {alpha}-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information. Protein Sci. 2004, 13 (7): 1908-1917.
Tusnady GE, Simon I: Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J Mol Biol. 1998, 283 (2): 489-506.
Saaf A, Johansson M, Wallin E, von Heijne G: Divergent evolution of membrane protein topology: The Escherichia coli RnfA and RnfE homologues. PNAS. 1999, 96 (15): 8540-8544.
Nilsson J, Persson B, von Heijne G: Consensus predictions of membrane protein topology. FEBS Lett. 2000, 486 (3): 267-269.
Humphrey W, Dalke A, Schulten K: VMD: visual molecular dynamics. J Mol Graph. 1996, 14 (1): 33-8, 27-8.
Hinttala R, Smeets R, Moilanen JS, Ugalde C, Uusimaa J, Smeitink JAM, Majamaa K: Analysis of mitochondrial DNA sequences in patients with isolated or combined oxidative phosphorylation system deficiency. J Med Genet. 2006, 43 (11): 881-886.
Simon DK, Friedman J, Breakefield XO, Jankovic J, Brin MF, Provias J, Bressman SB, Charness ME, Tarsy D, Johns DR, Tarnopolsky MA: A heteroplasmic mitochondrial complex I gene mutation in adult-onset dystonia. Neurogenetics. 2003, 4 (4): 199-205.
Andreu AL, Tanji K, Bruno C, Hadjigeorgiou GM, Sue CM, Jay C, Ohnishi T, Shanske S, Bonilla E, DiMauro S: Exercise intolerance due to a nonsense mutation in the mtDNA ND4 gene. Ann Neurol. 1999, 45 (6): 820-823.
Karadimas CL, Greenstein P, Sue CM, Joseph JT, Tanji K, Haller RG, Taivassalo T, Davidson MM, Shanske S, Bonilla E, DiMauro S: Recurrent myoglobinuria due to a nonsense mutation in the COX I gene of mitochondrial DNA. Neurology. 2000, 55 (5): 644-649.
Rahman S, Taanman JW, Cooper JM, Nelson I, Hargreaves I, Meunier B, Hanna MG, Garcia JJ, Capaldi RA, Lake BD, Leonard JV, Schapira AH: A missense mutation of cytochrome oxidase subunit II causes defective assembly and myopathy. Am J Hum Genet. 1999, 65 (4): 1030-1039.
Hanna MG, Nelson IP, Rahman S, Lane RJ, Land J, Heales S, Cooper MJ, Schapira AH, Morgan-Hughes JA, Wood NW: Cytochrome c oxidase deficiency associated with the first stop-codon point mutation in human mtDNA. Am J Hum Genet. 1998, 63 (1): 29-36.
Horvath R, Scharfe C, Hoeltzenbein M, Do BH, Schroder C, Warzok R, Vogelgesang S, Lochmuller H, Muller-Hocker J, Gerbitz KD, Oefner PJ, Jaksch M: Childhood onset mitochondrial myopathy and lactic acidosis caused by a stop mutation in the mitochondrial cytochrome c oxidase III gene. J Med Genet. 2002, 39 (11): 812-816.
Dumoulin R, Sagnol I, Ferlin T, Bozon D, Stepien G, Mousson B: A novel gly290asp mitochondrial cytochrome b mutation linked to a complex III deficiency in progressive exercise intolerance. Mol Cell Probes. 1996, 10 (5): 389-391.
Andreu AL, Hanna MG, Reichmann H, Bruno C, Penn AS, Tanji K, Pallotti F, Iwata S, Bonilla E, Lach B, Morgan-Hughes J, DiMauro S, Shanske S, Sue CM, Pulkes T, Siddiqui A, Clark JB, Land J, Iwata M, Schaefer J: Exercise Intolerance Due to Mutations in the Cytochrome b Gene of Mitochondrial DNA. N Engl J Med. 1999, 341 (14): 1037-1044.
Legros F, Chatzoglou E, Frachon P, Ogier De Baulny H, Laforet P, Jardel C, Godinot C, Lombes A: Functional characterization of novel mutations in the human cytochrome b gene. Eur J Hum Genet. 2001, 9 (7): 510-518.
Andreu AL, Checcarelli N, Iwata S, Shanske S, DiMauro S: A missense mutation in the mitochondrial cytochrome b gene in a revisited case with histiocytoid cardiomyopathy. Pediatr Res. 2000, 48 (3): 311-314.
Schuelke M, Krude H, Finckh B, Mayatepek E, Janssen A, Schmelz M, Trefz F, Trijbels F, Smeitink J: Septo-optic dysplasia associated with a new mitochondrial cytochrome b mutation. Ann Neurol. 2002, 51 (3): 388-392.
AnAge Database. [http://genomics.senescence.info/species/]
Acknowledgements
This work was supported in part by the Project POCTI/BSE/47559/2002 and PTDC/BIA-BDE/69144/2006 from the Portuguese Foundation for Science and Technology (FCT) and by the National Geographic Society Grant 7483-03. RF is funded by FCT fellowship SFRH/BPD/26769/2006. This project has been funded in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract N01-CO-12400. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Comments made by three anonymous referees improved a previous version of this manuscript.
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RF performed all phylogenetic, evolutionary and structure-function analyses and drafted the manuscript.
WEJ participated in the drafting and coordination of the study.
SJB participated in the drafting and coordination of the study.
MJR participated in the drafting and coordination of the study.
AA participated in the design, genetic analyses, drafting and coordination of the study.
All authors read and approved the final manuscript.
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Additional file 1: Supporting information. Saturation plots (S1), maximum likelihood tree (S2), number of strong positively selected amino acid properties plotted against the branch length (S3), number of strong positively selected amino acid properties across all sites in each mammalian mtDNA-encoded subunit of the respiratory chain (S4) and the sites indicated in Figure 10 in four elephant relatives and 6 alpaca relatives (S5). (PDF 2 MB)
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da Fonseca, R.R., Johnson, W.E., O'Brien, S.J. et al. The adaptive evolution of the mammalian mitochondrial genome. BMC Genomics 9, 119 (2008). https://doi.org/10.1186/1471-2164-9-119
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DOI: https://doi.org/10.1186/1471-2164-9-119