# Evolution of temperature optimum in *Thermotogaceae* and the prediction of trait values of uncultured organisms

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## Abstract

Quantitative characterization of the mode and rate of phenotypic evolution is rarely applied to prokaryotes. Here, we present an analysis of temperature optimum (*T* _{opt}) evolution in the thermophilic family *Thermotogaceae*, which has a large number of cultured representatives. We use log-rate-interval analysis to show that *T* _{opt} evolution in *Thermotogaceae* is consistent with a Brownian motion (BM) evolutionary model. The properties of the BM model are used to a establish confidence intervals on the unknown phenotypic trait value of an uncultured organism, given its distance to a close relative with known trait value. Cross-validation by bootstrapping indicates that the predictions are robust.

## Keywords

*Thermotogaceae*Phenotypic evolution Temperature optimum Microbial ecology Trait prediction Thermophiles

## Introduction

Temperature optimum (*T* _{opt}) is arguably one of the most conserved phenotypic traits. For a cell to achieve a specific *T* _{opt}, most or all of its genes need to be functional at this temperature, e.g., all the essential genes of a thermophilic organism must encode thermostable proteins. Furthermore, although genomes may evolve quickly through for example frequent events of horizontal gene transfer, this does not mean that phenotypes in general, and particularly not *T* _{opt}, change rapidly through successive generations. On the contrary, it seems reasonable to assume that *T* _{opt}, or more specifically protein thermostability and cell-membrane thermostability, represents a barrier to fixation of horizontally acquired genes. Although frequent horizontal gene transfer may result in genomes being a mosaic of genes from various lineages, the changes in *T* _{opt} over the same time-frame may be very small.

How fast is the evolution of *T* _{opt}? Addressing this question requires a measure of how *T* _{opt} changes as a function of evolutionary distance. The 16S rRNA gene has for decades been a useful marker for measuring evolutionary relatedness among prokaryotes (Woese and Fox 1977; Woese 1987) and is a cornerstone of present day prokaryotic taxonomy (Coenye et al*.* 2005; Stackebrandt et al*.* 2002). Phylogenetic clusters derived from 16S rRNA gene sequences commonly contain organisms with similar *T* _{opt} values, suggesting that although the 16S rRNA gene evolves extremely slowly, its resolution as a phylogenetic marker is sufficient to make inferences about *T* _{opt} evolution.

Phenotypic evolution can be divided into two components: the rate of evolution (tempo) and the mechanisms driving the rates (mode) (Kinnison and Hendry 2001; Simpson 1944). Quantitative characterization of these components is not only important for understanding the evolution, but may also have practical implications for microbial ecology. For example, knowing the mode of *T* _{opt} evolution in a microbial lineage may help constrain the expected *T* _{opt} values of uncultured organisms. This would be particularly useful for the organisms within thermophilic lineages as these organisms are typically detected in hydrothermal environments characterized by steep temperature gradients. For example, a few grams of a sample from the wall of a marine hydrothermal chimney may comprise micro niches with temperatures ranging from 0 to more than 300°C (Pagé et al*.* 2008). Thus, predicting *T* _{opt} of detected organisms from a temperature measurement of a bulk sample, if available at all, would be uninformative.

Here, we analyze the mode of *T* _{opt} evolution within the *Thermotogaceae* family, which has a large number of cultured type strains distributed in 7 described genera. Members of *Thermotogacea* are moderately thermophilic to hyperthermophilic, fermentative bacteria, typically isolated from hydrothermal systems. Our findings are consistent with the hypothesis that *T* _{opt} evolves according to a Brownian motion evolutionary model. We show how phenotypes of uncultured members of *Thermotogaceae* can be predicted based on known trait values of cultured relatives.

## Data and methods

### Database construction

We developed the software SPOT (sequence and phenotype organizing tool) to construct a database of 16S rRNA gene sequence information and *T* _{opt} information. SPOT, along with a users’s manual and the dataset analyzed here, can be downloaded from http://webber.uib.no/geobio/spot. Phenotypic data were compiled from the literature as follows: *Fervidobacterium pennavorans* (Friedrich and Antranikian 1996), *Fervidobacterium changbaicum* (Cai et al*.* 2007), *Fervidobacterium gondwanense* (Andrews and Patel 1996), *Fervidobacterium islandicum* (Huber et al*.* 1990), *Fervidobacterium nodosum* (Patel et al*.* 1985), *Geotoga petraea* (Davey et al*.* 1993), *Geotoga subterranea* (Davey et al*.* 1993), *Kosmotoga olearia* (DiPippo et al*.* 2009), *Marinitoga camini* (Wery et al*.* 2001), *Marinitoga hydrogenitolerans* (Postec et al*.* 2005), *Marinitoga piezophila* (Alain et al*.* 2002), *Marinitoga okinawensis* (Nunoura et al*.* 2007), *Petrotoga mexicana* (Miranda-Tello et al*.* 2004), *Petrotoga miotherma* (Davey et al*.* 1993), *Petrotoga mobilis* (Lien et al*.* 1998), *Petrotoga olearia* (L’Haridon et al*.* 2002), *Petrotoga sibirica* (L’Haridon et al*.* 2002), *Petrotoga halophila* (Miranda-Tello et al*.* 2007), *Thermosipho africanus* (Huber et al*.* 1989), *Thermosipho atlanticus* (Urios et al*.* 2004), *Thermosipho geolei* (L’Haridon et al*.* 2001), *Thermosipho japonicus* (Takai and Horikoshi 2000), *Thermosipho melanesiensis* (Antoine et al*.* 1997), *Thermotoga elfii* (Ravot et al*.* 1995), *Thermotoga hypogea* (Fardeau et al*.* 1997), *Thermotoga lettingae* (Balk et al*.* 2002), *Thermotoga maritima* (Huber et al*.* 1986), *Thermotoga naphthophila* (Takahata et al*.* 2001), *Thermotoga neapolitana* (Jannasch et al*.* 1988), *Thermotoga petrophila* (Takahata et al*.* 2001), *Thermotoga subterranea* (Jeanthon et al*.* 1995), *Thermotoga thermarum* (Windberger et al*.* 1989), and *Kosmotoga shengliensis* (Feng et al*.* 2010; Nunoura et al*.* 2010). Accession numbers of 16S rRNA gene sequences together with corresponding organism names and *T* _{opt} values are given in Supplementary Table 1.

### Brownian motion simulation

Temporal evolution of a single trait can be modeled as a Gaussian random walk, which assumes that the long-term dynamics of an evolving population is governed by the mean and variance of the distribution of evolutionary steps. In the limit of very small steps, the random walk approaches a one-dimensional Wiener process, and the latter is commonly referred to as Brownian motion model in the evolutionary biology literature (Felsenstein 1985). We simulated one-dimensional Brownian motions (BM) by calculating the cumulative sum of random deviates drawn from a normal distribution, whose mean and standard deviation are the governing parameters, describing the rate (standard deviation) and directionality (mean) of evolution in a phenotypic trait. We define a BM model as equivalent to an unbiased random walk with a mean of zero. Modeling evolutionary changes as random steps is motivated by our uncertainty regarding the role of different microevolutionary processes and the need to minimize mechanistic assumptions, but does not imply that evolution is random with respect to underlying causal factors.

### Linking *T* _{opt} variability with evolutionary distance

SPOT links an evolutionary distance matrix with phenotypic variability and produces a tab delimited output file including organism names, distances, and differences in *T* _{opt} (∆*T* _{opt}) for all unique pairs of organisms. Whenever *T* _{opt} values were reported as a range, differences in *T* _{opt} values between two strains were based on midpoint values. 16S rRNA distance matrices were constructed using the following approach: 16S rRNA genes sequences from all strains were aligned using the SINA webaligner (Pruesse et al*.* 2007) (http://www.arb-silva.de/aligner/). A distance matrix was generated from the alignment (*E. coli* positions 43–1371) using DNADIST with Jukes–Cantor correction and otherwise default settings as implemented in ARB (Ludwig et al*.* 2004). Comparisons involving branch lengths obtained from phylogenetic trees were done using in-house perl scripts.

### Construction of phylogenetic trees and calculation of independent contrasts

The SINA-generated 16S rRNA gene sequence alignment (see above) was exported to ARB (version 5.0) (Ludwig et al*.* 2004), where trees were produced using three different algorithms: (1) maximum likelihood by PhyML (Guindon and Gascuel 2003) applying the Hasegawa–Kishono–Yano nucleotide substitution model and where a discrete-gamma model (Yang et al*.* 1994) is implemented to accommodate rate variation among sites (four substitution rate categories were used and the gamma distribution parameter was estimated by maximizing the likelihood of the phylogeny), (2) maximum parsimony by Phylip DNAPARS (Felsenstein 1989), and (3) neighbor joining (Saitou and Nei 1987) with Jukes–Cantor (Jukes and Cantor 1969) correction. All trees were constructed from the same alignment (*E. coli* position 43–1371) and by applying a bacterial positional variability filter (pos_var_Bacteria_102), leaving 1357 valid columns. For other parameters in the tree constructions, default settings were used.

Trees were exported from ARB in newick format and converted to nexus trees using in-house perl scripts. Nexus trees were imported by Mesquite (version 2.72) (http://mesquiteproject.org/mesquite/mesquite.html), where phylogenetically independent contrasts (IC) (Felsenstein 1985) were calculated using the PDAP (Garland et al*.* 1999) module (version 1.14). In PDAP, phenotypic data from tip organisms in a phylogenetic tree are used to estimate the phenotypes of internal nodes. IC are calculated as (*T* _{opt(node1)} − *T* _{opt(node2)})([*L* _{branch}]^{−0.5}), where node1 and node2 are two sister nodes in the tree and* L* _{branch} is a corrected branch length between the sister nodes (see Garland et al*.* (2005) for a worked example).

### A possible method for prediction of trait values of uncultured organisms

*X*

_{i}be the value of a continuous phenotypic trait

*X*in strain i, then a BM evolutionary model predicts that at a certain evolutionary distance (

*D*), independent observations of (

*X*

_{i}−

*X*

_{j}) are normally distributed with expectation zero and variance proportional to

*D*(Felsenstein 1985). Thus, observations of (

*X*

_{i}−

*X*

_{j}) at different distances can be standardized by dividing by the square root of the distance, obtaining values of

*S*= (

*X*

_{i}−

*X*

_{j}) (

*D*

^{−0.5}), which are also normally distributed with expectation 0 and variance

*σ*

^{2}. |

*S*| will have a half-normal distribution with a standard deviation,

*s*, which can be estimated from a sample of independent pairs of organisms with known trait values and known distances. A given value of

*s*can further be used to estimate

*σ*from the relationship

*s*

^{2}=

*σ*

^{2}(1 − 2/π). Given a 95% confidence interval (−1.96

*σ*, 1.96

*σ*) of

*S*, the confidence interval of (

*X*

_{i}−

*X*

_{j}) is [−1.96

*σ*(

*D*

^{0.5}), 1.96

*σ*(

*D*

^{0.5})]. Therefore, if we have a pair of organisms (i, j) with known evolutionary distance

*D*, but where only one of the trait values (

*X*

_{j}) is known, the 95% confidence interval for the unknown trait value (

*X*

_{i}) can be expressed as

### Cross-validation

Cross-validation of each predictive equation was performed by iterative re-sampling (5,000 bootstrap replicates) of the original dataset, randomly assigning species into a training set (17 species) and a validation set (16 species). In each iteration, *σ* values were estimated from the random training set and used in Eq. 1. Next, each organism in the validation set was paired with a random organism in the training set and their observed *T* _{opt} difference (∆*T* _{opt}) was compared with the predicted value. A prediction was considered to be erroneous when 1.96*σ* (*D* ^{0.5}) < ∆*T* _{opt}. Error rates were defined as the total proportion of erroneous predictions.

## Results and discussion

### The mode of *T* _{opt} evolution in *Thermotogaceae*

*Thermotogaceae*dataset. The resulting confidence interval on the BM slope [−0.644 −0.363] can be used to test the null hypothesis that the observed

*T*

_{opt}evolution does not significantly deviate from that expected under a BM model.

Our dataset contains 33 members of *Thermotogaceae*, yielding 528 unique pairs of organisms, with corresponding DNADIST-derived evolutionary distances (16S_{dist}) and differences in *T* _{opt} (∆*T* _{opt}) (Fig. 1d). For each pair, we calculated an average rate of *T* _{opt} evolution defined as ∆*T* _{opt}/16S_{dist}, which shows the characteristic abrupt increase in rate at small distances (Fig. 1e). Inaccuracies in the reported *T* _{opt} values, many of which are binned into 5 degree intervals, would pose a serious problem only if that inaccuracy varies in a systematic manner on the phylogeny, e.g., if *T* _{opt} is measured differently within one subgroup of *Thermotogaceae* than in other subgroups. We do not have any reason to suspect that this is the case. Instead, we assume that over- and underestimation of *T* _{opt} is randomly distributed over the phylogeny. However, binning may have an effect on the LRI analysis by underestimating *T* _{opt} variance, potentially biasing the slope estimates toward stasis. In our view, true *T* _{opt} values are likely to fall within a 5-degree interval around the reported value. To account for this uncertainty, we added noise to the *T* _{opt} values in the form of uniformly random deviates drawn from the interval [−2.5 2.5]. This allows us to calculate a mean LRI slope (−0.493) and 95% confidence interval [−0.542 −0.442] by bootstrapping (5,000 replicates), which more realistically accounts for the inaccuracy of the *T* _{opt} data (Fig. 1f). The range of estimated LRI slopes for *Thermotogaceae* falls well within the 95% confidence interval for the BM model, consistent with the hypothesis that *T* _{opt} evolution occurs as BM.

A problem with applying LRI to all possible pairs is that different pairs of organisms are not independent because related organisms share parts of their evolutionary history (phylogenetic autocorrelation). In order to evaluate whether the LRI results are biased by such autocorrelation, we performed the LRI analysis on independent contrasts (IC) generated by three different algorithms (Fig. 1d–f). Since the number of IC is much smaller than the DNADIST-based dataset, the BM 95% confidence interval is correspondingly much wider [–0.948 −0.067]. The LRI slopes for all three IC reconstructions fall within that interval. Moreover, analyses in PDAP (Garland et al*.* 1999) showed no significant correlation between IC and the square root of branch length, further supporting our conclusion.

### A framework for predicting *T* _{opt} in *Thermotogaceae*

*T*

_{opt}predictions. For example, all 74 pairs observed to have 16S

_{dist}<0.10 show ∆

*T*

_{opt}<10°C. Intuitively, one might infer that the

*T*

_{opt}of an uncultured organism is likely to differ by <10°C from the

*T*

_{opt}of a cultured close relative given that 16S

_{dist}<0.10. The goal of this study is to assign quantitative measures of confidence on such predictions. LRI analysis shows that the evolution of

*T*

_{opt}in

*Thermotogaceae*is consistent with a BM model. We can therefore take advantage of the properties of the BM model to obtain a general prediction of

*T*

_{opt}according to Eq. 1 by letting X represent

*T*

_{opt}values. As explained earlier,

*σ*can be estimated in two ways: (1) a non-phylogenetic approach based on the values of (

*X*

_{k}−

*X*

_{l}) (

*D*

^{−0.5}), where

*X*

_{k}and

*X*

_{l}represent known

*T*

_{opt}values for two strains k and l, respectively, and

*D*is 16S

_{dist}. Based on all 528 unique pairs, we found an estimated value of

*σ*= 26.3. (2) A phylogenetic approach, whereby

*σ*can be estimated from the square root of the variance of IC values. Depending on the algorithm used to obtain the evolutionary distances, this approach yields

*σ*estimates in the range 29.8–33.3 (Table 1). The consistency between the different

*σ*estimates is encouraging given the different assumptions and underlying distance measures.

Overview of *T* _{opt} prediction equations generated from four different approaches to measuring evolutionary distance

Methods requiring phylogenetic reconstruction | Non-phylogenetic method | |||
---|---|---|---|---|

Algorithm used to obtain evolutionary distances ( | Maximum likelihood | Maximum parsimony | Neighbor-joining | DNADIST |

| Branch length | Branch length | Branch length | 16S |

Variance of [∆ | 251.1 | |||

| 29.8 | 32.1 | 33.3 | 26.3 |

Prediction | | | | |

Cross-validation | ||||

Mean of | 25.3 | 27.9 | 27.5 | 26.3 |

Standard deviation of | 6.1 | 6.7 | 5.6 | 0.6 |

Median error rate | 0 | 0 | 0.06 | 0 |

Mean error rate | 0.06 | 0.04 | 0.07 | 0.04 |

95th percentile error rate | 0.25 | 0.25 | 0.25 | 0.13 |

### Cross-validation

Cross-validation by bootstrapping shows that the sigma values are robust to subsampling and stable regardless of the method used (Table 1). Furthermore, median *T* _{opt} prediction error rates are generally lower than the theoretical expectation of 0.05 (Table 1). Note that the error rate distributions are highly skewed (hence we report median values) with rare instances of large error rates (>0.25).

### Constraining the *T* _{opt} in ‘mesotogas’

*Thermotogaceae*family are either moderate thermophiles, thermophiles, or hyperthermophiles. However, 16S rRNA gene sequences from members of this family have been detected in mesothermic environments or enrichment cultures. The corresponding lineages have informally been designated as ‘mesotogas’ (Nesbø et al

*.*2006, 2010). In the absence of a mesophilic isolate, their existence remains unproven. Nevertheless, we used the equations in Table 1 to address the following questions: are uncultured mesotogas unlikely to be mesophiles given their relatedness to thermophilic organisms? Are all

*Thermotogaceae*detected in mesothermic environments likely to be mesophiles? Here, we analyzed a dataset recently published by Nesbø et al

*.*(2010) describing five putative lineages of mesotogas. Four of these (M1, M2, M4, and M5) are represented by at least one near full-length 16S rRNA gene sequence. Using the equations in Table 1, we predicted confidence intervals of

*T*

_{opt}values of mesotoga members given their evolutionary distance to the closest cultured relative (Table 2). The predictions are of limited precision because the mesotogas are only distantly related to the described species (DNADIST and branch length values were in the range of 0.11–0.26). Yet, our results indicate that it is reasonable to assume that mesotoga lineages M4 and M5 comprise mesophilic organisms, whereas lineages M1 and M2 seem to be at least moderately thermophilic. It is still possible that lineages M1 and M2 are mesophilic and have temperature optima lower than predicted (Table 2), but if they are, the rate of

*T*

_{opt}evolution in the branches leading to these lineages are unexpectedly high under a BM model.

Predicted confidence intervals of *T* _{opt} from selected members of ‘mesotogas’

Representative organism (accession number) | Lineage | Predicted | |||
---|---|---|---|---|---|

Maximum likelihood | Maximum parsimony | Neigbor-joining | DNADIST | ||

HM003102 | M1 | 44–86 | 42–88 | 43–87 | 46–84 |

HM003104 | M2 | 43–87 | 43–87 | 43–87 | 46–84 |

EU735623 | M4 | 33–96 | 28–97 | 32–88 | 36–84 |

CU921025 | M4 | 34–95 | 31–98 | 38–92 | 41–89 |

HM003101 | M5 | 21–79 | 22–78 | 23–76 | 27–73 |

## Conclusion

The evolution of *T* _{opt} within *Thermotogaceae* is consistent with a Brownian motion model of phenotypic evolution. Based on this model, we developed a general method for *T* _{opt} prediction of uncultured members of this family. Cross-validation shows that the predictions are accurate (low error rates), stable under different phylogenetic reconstructions, and robust to taxonomic sampling. Similar analyses can be performed on any continuous characteristic (e.g., regarding pressure, pH, salinity, growth rate, genome size, and % GC content) and within any microbial group, which would greatly enhance the value of functional studies and provide valuable insight into evolutionary dynamics.

## Notes

### Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

## Supplementary material

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