Thermophilic organisms live at 50 °C to over 100 °C, temperatures that would quickly denature most proteins from mesophiles. There is considerable interest in determining what enables proteins from thermophiles to function at high temperatures, both for the practical benefit of engineering proteins for high-temperature industrial processes and as an evolutionary and biochemical puzzle.

One way to investigate whether some amino acids are more favorable than others at higher temperature is to compare the overall proportions of amino acids in protein sequences from prokaryotes living at different temperatures (Cambillau and Claverie 2000; Fukuchi and Nishikawa 2001; Chakravarty and Varadarajan 2002; Singer and Hickey 2003; Berezovsky et al. 2007). An amino acid that is more abundant in species living at higher temperatures is then interpreted to be adaptive to the higher temperatures. However, a major problem with this approach is that prokaryotes vary widely in genome-wide GC content, and amino acids with GC-rich codons are generally more abundant in organisms with GC-rich genomes (Lobry 1997; Singer and Hickey 2000). There is conflicting evidence about whether genome-wide GC content shows any relationship with habitat temperature (Musto et al. 2006; Wang et al. 2006), but the strong association of GC content and amino acid abundance will obscure any relationship between temperature and amino acid abundance if the variation in GC content is ignored.

The effects of temperature and GC content can be separated using multivariate statistical techniques, such as principal component analysis (Kreil and Ouzounis 2001; Saunders et al. 2003), correspondence analysis (Tekaia et al. 2002; Lobry and Chessel 2003; Tekaia and Yeramian 2006; Boussau et al. 2008; Puigbò et al. 2008), and other techniques (Naya et al. 2006; Zeldovich et al. 2007). However, these approaches suffer from “phylogenetic pseudoreplication”; they treat multiple species from the same clade and same habitat as if they were independent samples, and it has long been known that this can cause serious statistical problems (Felsenstein 1985; Harvey and Pagel 1991). To illustrate why this is a problem, imagine biologists who were interested in temperature adaptation of terrestrial vertebrates. If those biologists surveyed vertebrates from a variety of habitats and looked for associations with temperature, they would see a higher proportion of species that shed their skin living in warmer areas. However, it would be erroneous to conclude from this that shedding skin is an adaptation to high temperature; the association would merely result from sampling large numbers of Squamata (lizards and snakes) in warm areas and few squamates in cold areas. Similarly, in studies of temperature and amino acid composition, some clades are found predominantly among thermophiles, and some are predominant among mesophiles; for example, of the 204 species studied by Zeldovich et al. (2007), 63% of the thermophiles and 5% of the mesophiles are archaea, whereas 0% of the thermophiles and 54% of the mesophiles are proteobacteria. A multivariate statistical technique that treated each species as an independent data point could produce an apparent association of particular amino acids with higher temperatures, when in reality that association might result from a difference between clades that may have nothing to do with temperature.

A second form of evidence used to compare amino acid composition in mesophiles and thermophiles is substitutional asymmetry (Argos et al. 1979; Haney et al. 1999; McDonald et al. 1999). Protein sequences from one mesophile and one thermophile are aligned, and the observation of more aligned sites with amino acid A in the mesophile and B in the thermophile than the opposite pattern provides evidence that B is favored over A in the higher temperature organism. Because only aligned sites in homologous proteins are considered, the effect of gain or loss of proteins of different amino acid composition does not obscure the results. In addition, each mesophile–thermophile pair of species can be phylogenetically independent of others that have been compared, an important consideration when using comparative methods to infer adaptation. (To say that mesophile–thermophile pair A and B are “phylogenetically independent” of other pairs means that A and B are more closely related to each other than either is to any of the other species in the data set.) This approach has found extensive evidence for substitutional asymmetry (Haney et al. 1999; McDonald et al. 1999; McDonald 2001; Nishio et al. 2003; Mizuguchi et al. 2007), but the problem remains that for those pairs of amino acids whose codons have different GC content, overall differences in GC content between the mesophile and thermophile could still be the cause of substitutional asymmetry. Here, I use logistic regression of the proportion of substitutions in one direction versus the overall difference in GC content to predict the substitutional asymmetry in a pair of species with identical genomic GC content. This method should help determine whether amino acids that are favored at higher temperatures share biochemical properties.

If substitutional asymmetry between mesophilic and thermophilic proteins results from temperature adaptation based on the fundamental biochemical properties of the amino acids, the same patterns should be found in all mesophile–thermophile comparisons after controlling for differences in GC content. Differences in other aspects of the environment, such as salinity, hydrostatic pressure, pH, oxygen, and nutrient source, could cause patterns of asymmetry that are unrelated to temperature and therefore different in different mesophile–thermophile pairs. In addition, biosynthetic costs of amino acids are high enough to cause selection on amino acid usage (Akashi and Gojobori 2002; Seligmann 2003; Heizer et al. 2006; Swire 2007), so organisms which differ in biosynthetic pathways, or which differ in whether they are autotrophic or heterotrophic for a particular amino acid, may have different patterns of substitutional asymmetry. A second goal of this paper is to see how consistent the patterns of substitutional asymmetry are among different species, which may help determine how much of the asymmetry is due to temperature adaptation and how much is due to other factors.

Each of the nine mesophile–thermophile species pairs exhibits a large amount of substitutional asymmetry; for most pairs of amino acids, there are more homologous sites with one amino acid in the mesophile and the other amino acid in the thermophile than the opposite. Substitutional asymmetry has been previously observed in small numbers of proteins from Methanococcus versus Methanocaldococcus (Haney et al. 1999; McDonald et al. 1999), Bacillus versus Geobacillus (McDonald et al. 1999), and Deinococcus versus Thermus (McDonald 2001). Here, I use translated protein sequences from the entire genomes of these species pairs and add six additional mesophile–thermophile pairs from a broad variety of habitats.

Differences in genome-wide GC contents are one cause of substitutional asymmetry; all the species pairs used here differ to some degree in GC content, and it has long been known that amino acids with GC-rich codons are more common in species with GC-rich genomes (Lobry 1997; Singer and Hickey 2000). It is not clear whether differences in genome-wide GC content are caused by selection or mutational bias (Rocha and Danchin 2002; Lind and Andersson 2008), and it is not clear to what extent increased habitat temperatures cause increased GC contents (Musto et al. 2006; Wang et al. 2006). What is clear is that any attempt to identify selection on amino acids as a cause of substitutional asymmetry must remove the effects of GC content.

Here, logistic regression modeling is used to control statistically for the effects of differing GC content, with the difference in GC content as the independent variable and the direction of substitution as the dependent variable. For the majority of amino acid pairs, the logistic regression predicts that a mesophile–thermophile pair of species that did not differ in GC content would exhibit extensive substitutional asymmetry. The significant intercepts in the logistic regression models mean that the preferences for one amino acid over another are fairly consistent across the nine pairs of species.

Substitutional asymmetry in one mesophile–thermophile pair could be caused by any number of habitat differences; for example, the mesophile Methanococcus maripaludis was isolated from a salt marsh (Jones, Paynter, and Gupta 1983), whereas the thermophile M. jannaschii was originally isolated from a deep-sea vent 2,600 m below the ocean surface (Jones, Leigh, et al. 1983). A difference in hydrostatic pressure may favor some amino acids over others (Di Giulio 2005); if hydrostatic pressure were an important selective factor, M. maripaludis and M. jannaschii would have patterns of asymmetry different from the other mesophile–thermophile pairs, which do not differ in the hydrostatic pressure of their habitats. The consistency of the patterns of asymmetry across species pairs suggests that much of the asymmetry results from selection caused by the different habitat temperatures.

Although the patterns of asymmetry are consistent enough across species pairs to produce logistic regression models with significant intercepts, the amounts of asymmetry in each species pair are not exactly as predicted by the logistic regression; many amino acids are favored more or less strongly in some species pairs than would be expected. The optimal temperatures of the species pairs differ by different amounts, from 15 to 55 °C, so it would have been startling if they all exhibited the exact same amount of asymmetry. The species pairs differ in how recently they diverged from a common ancestor, and the species pairs also vary in other aspects that may affect selection on amino acid use: aerobic versus anaerobic; autotrophic versus heterotrophic; marine, freshwater, and terrestrial; and deep sea versus shallow water. Species pairs in which the ancestral species was thermophilic and one lineage then adapted to lower temperatures may show different patterns of temperature adaptation than species pairs in which the ancestor was mesophilic and one lineage adapted to higher temperatures (Berezovsky and Shakhnovich 2005). There is also increasing evidence that biosynthetic costs may affect amino acid use (Akashi and Gojobori 2002; Seligmann 2003; Heizer et al. 2006; Swire 2007), and the costs of particular amino acids will depend on factors that may be unrelated to temperature, such as the biosynthetic pathways used (for autotrophs) and environmental availability and uptake costs (for heterotrophs). Including all the possibly relevant variables when there are only nine species pairs would result in a logistic model that was completely overdetermined, with many spurious correlations; separating the substitutional asymmetry caused by temperature adaptation from the asymmetry resulting from other causes will require examining the genomes of a much larger number of mesophile–thermophile species pairs than currently available.

These results show that amino acids with greater hydrophobicity (higher transfer free energy) tend to be preferred in thermophiles, which is consistent with several earlier studies (Argos et al. 1979; Gromiha, Oobatake, Kono, et al. 1999; Haney et al. 1999; Tekaia et al. 2002; Nakashima et al. 2003; Sadeghi et al. 2006; Berezovsky et al. 2007). There are, however, numerous exceptions to this rule. This is consistent with previous research that has failed to identify a single physicochemical property of the amino acids that would explain all the differences in amino acid abundance between mesophiles and thermophiles (Böhm and Jaenicke 1994; Zhou et al. 2008). One possible explanation is that thermal adaptation of amino acids is based on complicated tradeoffs between different properties (Gromiha, Oobatake, and Sarai 1999). Another possibility is that the cost of synthesizing amino acids plays a major role; the relative synthesis costs of amino acids change as temperatures increase (Amend and Shock 1998), and amino acids with lower synthesis costs tend to be more abundant, even in heterotrophs (Swire 2007). Values for the cost of synthesis of each amino acid in each species at a variety of temperatures are not available; as this information accumulates, it may become possible to understand the role that relative biosynthetic costs of amino acids play in temperature adaptation of proteins.

There are numerous reports of charged amino acids being more common in thermophiles than in mesophiles (Cambillau and Claverie 2000; Das and Gerstein 2000; Szilágyi and Závodszky 2000; Fukuchi and Nishikawa 2001; Vielle and Zeikus 2001; Chakravarty and Varadarajan 2002; Tekaia et al. 2002; Nakashima et al. 2003; Suhre and Claverie 2003; Sadeghi et al. 2006; Berezovsky et al. 2007). That pattern is not apparent here; of 47 significant intercepts in the logistic regression involving one charged amino acid (arginine, aspartic acid, glutamic acid, and lysine) and one noncharged amino acid, 24 have the charged amino acid becoming more common in the thermophiles, but 23 have the charged amino acid becoming less common in the thermophiles (table 2). If histidine, which is weakly charged at physiological pH, is included in the charged amino acids, the result is the same: Of 57 significant intercepts, 28 have the charged amino acid becoming more common in the thermophiles, but 29 have the charged amino acid becoming less common in the thermophiles. Most of the studies reporting increased proportions of charged amino acids in thermophiles have relied heavily on hyperthermophiles, which have optimum growth temperatures of 85 °C to >100 °C, whereas the nine species pairs used here include only one hyperthermophile, M. jannaschii, with an optimum growth temperature of 85 °C. It may be that increasing the overall proportion of charged amino acids is only an important adaptation at very high temperatures.

Supplementary tables 1 and 2 are available at Genome Biology and Evolution online (http://www.oxfordjournals.org/our_journals/gbe/).