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Weak phylogenetic signal in physiological traits of methane-oxidizing bacteria
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Sascha Krause1,2*, Peter M. van Bodegom3, Will K. Cornwell3 and Paul L.E. Bodelier1
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Wageningen, The Netherlands
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Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA
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Department of Ecological Sciences, Subdepartment of Systems Ecology, VU University of
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Amsterdam, The Netherlands
Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW),
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*Corresponding author. Department of Chemical Engineering, Benjamin Hall IRB, Room 440,
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University of Washington, 616 NE Northlake Place, Seattle, WA 98195, USA. Phone: +1-206-
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616-6954. E-mail: smb.krause@gmx.com.
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Running title: The Phylogenetic signal in microbial traits
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Keywords: Traits, phylogenomics, methane oxidation, modeling, horizontal gene transfer,
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microorganisms
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Abstract
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The presence of phylogenetic signal is assumed to be ubiquitous. However, for microorganisms
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this may not be true given that they display high physiological flexibility and have fast
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regeneration. This may result in fundamentally different patterns of resemblance, i.e. in variable
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strength of phylogenetic signal. However, in microbiological inferences, trait similarities and
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therewith microbial interactions with its environment are mostly assumed to follow evolutionary
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relatedness. Here we tested whether indeed a straightforward relationship between relatedness
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and physiological traits exists for aerobic methane oxidizing bacteria (MOB). We generated a
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comprehensive dataset that included 30 MOB strains with quantitative physiological trait
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information. Phylogenetic trees were built from the 16S rRNA gene, a common phylogenetic
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marker, and the pmoA gene which encodes a subunit of the key enzyme involved in the first step
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of methane oxidation. We used a Blomberg’s K from comparative biology to quantify the
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strength of phylogenetic signal of physiological traits. Phylogenetic signal was strongest for
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physiological traits associated to optimal growth pH and temperature indicating that adaptations
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to habitat are very strongly conserved in MOB. However, those physiological traits that are
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associated with kinetics of methane oxidation had only weak phylogenetic signals and were more
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pronounced with the pmoA than with the 16S rRNA gene phylogeny. In conclusion, our results
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give evidence that approaches based solely on taxonomical information will not yield further
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advancement on microbial eco-evolutionary interactions with its environment. This is a novel
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insight on the connection between function and phylogeny within microbes and adds new
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understanding on the evolution of physiological traits across microbes, plants and animals.
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Introduction
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Phylogenetic signal generally describes a pattern where evolutionary related taxa share more
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similar traits (phenotypic or genetic) than those with a more ancient common ancestry, without
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implying about the processes responsible for the resemblance (Blomberg et al., 2003; Srivastava
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et al., 2012). In a comprehensive meta-analysis, Freckleton and colleagues (2002) analyzed 103
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faunal traits and demonstrated that about 60 % of traits displayed significant evidence of
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phylogenetic signal. Similar conclusions have been drawn for plant traits (Webb et al. 2002),
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suggesting that it is a universal phenomenon. Hence, phylogenetic signal has been used to predict
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species traits from its position in a phylogenetic tree and trait values of close relatives (Guénard
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et al., 2011) or infer about speed and type of trait evolution (Ackerly et al., 2000; Ackerly,
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2009). In microbial ecology, the proposition of phylogenetic signal has been widely used to
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make inferences on microbial processes and microbial ecosystem functioning without knowing
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species contributions to the processes or assumed a straightforward correlation between trait
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distribution and phylogeny (Fierer and Jackson, 2006; Srivastava et al., 2012; Zhang et al. 2013).
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However, while in higher organisms, traits may be shared among taxa with similar
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ecological niches and in some cases they may be preserved over many generations and through
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evolutionary time (Crisp & Cook, 2012), this is different in microbes where evolutionary change
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can be quite rapid. They have much shorter generation times (Wiser et al., 2013) and can adapt
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quickly to new conditions by interchanging genetic elements (Popa & Dagan, 2011). Such
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characteristics may, in general, lead to a weaker phylogenetic signal as has been shown
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particularly for traits that are prone to high plasticity (e.g. behavior or physiology) (Blomberg et
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al., 2003; Hertz et al., 2013).
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Hence, microorganisms differ in some evolutionary processes compared to plants and animals
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(e.g. horizontal gene transfer, physiological flexibility), and this may lead to less phylogenetic
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signal of microbial traits, in general.
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However, it is challenging to quantify traits in complex environmental microbial communities
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(Wallenstein & Hall, 2012) where it is difficult to link traits to individual bacterial lineages from
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the vast pool of microbial diversity, of which the majority is geno- as well as phenotypically not
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described. As a consequence, phylogenetic trait-signals in microorganism have by far been less
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well studied and have mainly been focused on the presence/absence of traits (e.g. Martiny et al.,
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2012; Zimmerman et al., 2013), and lacked a quantitative evolutionary framework.
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In this situation, a model system may inform us on the relationships between phylogeny and
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microbial traits. Aerobic methane-oxidizing bacteria (MOB) may constitute such model system,
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because they have limited phylogenetic diversity and catalyze a specific ecosystem function, i.e.
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methane oxidation. Many taxa have been isolated from various environments and many traits of
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these organisms have been measured under controlled conditions. They belong to the
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Proteobacteria and Verrucomicrobia. The proteobacterial MOB are represented in the families
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Methylococcaceae (γ-Proteobacteria) and Methylocystaceae and Beijerinckiaceae (α-
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Proteobacteria) (Nazaries et al., 2013). The first step in the aerobic methane oxidation pathway is
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catalyzed by two forms of a methane monooxygenase (MMO). The soluble methane
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monooxygenase (sMMO) is only found in some MOB, while the particulate membrane bound
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methane monooxygenase (pMMO) is present in all MOB, except for Methylocella and
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Methyloferula species (Dedysh et al., 2000; Vorobev et al., 2010). Therefore, the pmoA gene
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encoding a subunit of the pMMO has been commonly used as gene for reconstructing MOB
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phylogenies linking function and identity. The use of this gene related to the metabolic pathway
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may enhance the phylogenetic signal of microbial metabolic traits. On the other hand, the 16S
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rRNA encodes the small subunit of prokaryotic ribosomes and is commonly used in
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reconstructing phylogenies and thus provides a general test of phylogenetic signal in microbial
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traits.
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We used continuous quantitative physiological traits measured for representative cultured and
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described MOB strains to address the following questions: (1) do physiological traits of MOB
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exhibit a phylogenetic signal, i.e. do closely related taxa resemble each other more than distant
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taxa and (2) do genes associated with the specific function of methane oxidation increase the
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phylogenetic signal of physiological traits related to this pathway? The strength of phylogenetic
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signal has never been explicitly tested for microbes but has important implications for the study
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of eco-evolutionary interactions of microbes with its environment. For our tests, we applied tools
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from comparative biology which allow comparing multiple traits over different phylogenies.
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Materials and methods
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Data collection
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We generated a dataset which contained general physiological traits as well as physiological
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traits specifically related to methane oxidation of isolated MOB and corresponding pmoA gene
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and 16S rRNA gene sequences. The analyses focused on continuous physiological traits (i.e. pH
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range, optimal pH for growth, temperature range, optimal temperature for growth) and
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physiological traits represented by methane oxidation kinetics (i.e. specific affinity [Vmax/Km]
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and Michaelis constant Km [apparent]) (Table 1). In addition, GC content, a trait known to be
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linked to taxonomy, was used as a reference to detect phylogenetic signal. On the basis of GC-
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content and the physiological traits mentioned above, a database was constructed for 23 MOB
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strains (Table 1). Although phenotypic plasticity in individual species and methodological
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impacts can be assumed to be low for pH preference, temperature preference and GC content, it
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can be high for kinetic traits of MOB (Knief & Dunfield, 2005). Therefore, for kinetic traits , we
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restricted the analysis to one data set (Knief & Dunfield, 2005), where kinetic parameters of 10
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isolated MOB strains had been measured under controlled, similar laboratory conditions which is
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absolutely necessary to compare apparent enzyme kinetics (Table 2). We are not aware of similar
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datasets for other groups of microorganisms.
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Phylogenetic tree construction
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Sequences were retrieved from the Silva database (Quast et al., 2013) and an unpublished
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manually curated pmoA database using the ARB software package (Ludwig et al., 2004). In total,
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30 different aligned pmoA and 16S rRNA gene sequences matched the physiological trait data.
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Based on these DNA sequences we constructed phylogenetic trees for pmoA (partial, 391bp) and
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16S rRNA (partial, 1399bp) (Fig. S1, S2). We used the maximum likelihood (ML) tree estimation
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method. Prior to phylogenetic tree construction, we performed jModeltest (Posada, 2008) as
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implemented in the phangorn package of the statistical software R (R Development Core Team,
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2013) to find the most appropriate model of DNA substitutions. The DNA substitution model
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“GTR” was used to create ML phylogenetic trees (Tavaré, 1986). In brief, the “GTR” model
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assumes variable base frequencies and includes a time reversible symmetric substitution matrix.
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ML tree constructions were carried out with 1000 bootstraps. All analyses were performed using
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the ape and phangorn package in the statistical software R (R Development Core Team, 2013).
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The phylogenetic signal of physiological traits
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There are several methods available to detect phylogenetic signal which have been recently
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reviewed in Münkemüller and colleagues (2012). In this study we focused on Blomberg’s K, a
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method that quantifies the strength of the phylogenetic signal. Blomberg’s K is based on the
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assumption of a Brownian motion (BM) model of trait evolution. According to this model, trait
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evolution proceeds in a random walk and the variance between species increases proportionally
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to the time they split from a common ancestor, which is the summed branch length from root to
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tip (Revell et al., 2008). Briefly, it calculates the ratio of the mean squared error of the tip data
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measured from the phylogenetic mean (MSE0), to the mean squared error of the data calculated
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using the variance-covariance matrix derived from a given phylogeny (MSE) (Blomberg et al.,
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2003; Münkemüller et al., 2012). Since the ratio of MSE0/MSE is sensitive to tree size and shape,
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the observed ratio is standardized by the expected ratio, which is predicted under the assumption
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of BM evolution (Blomberg et al., 2003):
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K  observed
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K values of one indicate an evolution of traits following BM. K values above one indicate that
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taxa are more similar than expected and K values below one indicate more divergence than under
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BM evolution of a given phylogeny (Blomberg et al., 2003).
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We tested for presence of a phylogenetic signal using a simple randomization procedure as
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implemented in Blomberg’s K calculations. In brief, an observed trait distribution on a
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phylogenetic tree is compared with the trait distribution randomly shuffled across tips of that
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phylogeny. The null hypothesis in this analysis is that closely related species do not share similar
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patterns (Blomberg et al., 2003). Since Blomberg’s K values can display high variability
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(Münkemüller et al., 2012) we performed the analysis of physiological traits for each of the
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thousand bootstrap trees generated during ML tree construction. Box plots were created to
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visualize the variation in signal strength.
MSE 0
MSE 0
exp ected
MSE
MSE
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Because Blomberg’s K strongly relies on branch length information we additionally performed
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the test of Abouheif’s Cmean (1999) which provides an estimate of phylogenetic signal in a
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quantitative trait solely based on tree topology. In brief, a given trait is ordered along a
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phylogeny and the sum of the successive squared differences between observations is calculated.
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This sum is compared to tabulated critical values and subsequently used to test for significance
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(Abouheif, 1999). All phylogenetic signal calculations were performed using the picante, geiger,
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adephylo and the ade4 package in the statistical software R (R Development Core Team, 2013).
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Results
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We applied a set of tools from comparative biology to describe and quantify multiple traits over
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two marker gene phylogenies. Of all traits, the optimal growth temperature and optimal growth
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pH displayed the highest phylogenetic signal for the pmoA phylogeny (Fig. 1 A). For the 16S
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rRNA phylogeny, optimal growth temperature and GC content displayed highest values for
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Blomberg’s K (Fig. 1 B).
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Physiological traits related to methane oxidation displayed phylogenetic signals which were
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generally more pronounced with the pmoA than with the 16S rRNA phylogeny (Fig. 1). On the
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other hand, the Blomberg’s K randomization procedure only showed the presence of a
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phylogenetic signal for optimal growth temperature and optimal growth pH which was consistent
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over all phylogenies (Table S1). In addition, growth pH range and GC displayed presence of
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significant phylogenetic signal, but only with the 16S rRNA phylogenies (Table S1). The branch
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length independent test of Abouheif’s Cmean was less conservative and consistently revealed the
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presence of phylogenetic signal over all phylogenies for optimal pH, temperature for growth, pH
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range for growth and GC content, but for none of the physiological traits related to methane
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oxidation (Table S2).
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Discussion
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The phylogenetic signal of physiological traits
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The degree to which the traits that affect ecosystem processes show phylogenetic signal is a
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research question that extends across taxa from plant to animals to microbes (Díaz et al., 2013)
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Cornelissen & Cornwell 2014), but in contrast to e.g. plants (Swenson, 2011), it has hardly been
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tested for microbes. In this study we tested for the presence as well as strength of phylogenetic
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signal in microbial traits using MOB as a model system. We found a weak, i.e. less than
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expected under a Brownian motion model, but non-random phylogenetic signal for physiological
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traits of MOB. There are a number of possible explanations for this pattern including relatively
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rapid recent evolution (O'Meara, 2012; Pennell & Harmon, 2013) and/or a mean-reverting
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process (Butler, 2006). It has been suggested that adaptive evolution and repeated changes in
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selective environmental pressures are responsible for such low phylogenetic signals, which have
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been found before in animals and plants (Blomberg et al., 2003; Ackerly, 2009). Likewise,
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microorganisms may also respond to fluctuating environments either by rapid adaptive evolution
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(Cooper, 2002) or by phenotypic variation (Ackermann, 2013). Close relatives do to some degree
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resemble each other, but recent evolution of these crucial traits (for a similar plant example see
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Ackerly, 2009) may have prevented these traits from showing strong phylogenetic signal.
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The strongest phylogenetic signals that we did find were for optimal growth pH. This trait
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contains polygenic properties (e.g. transporters, membranes) that require a lot of adaptation. As a
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consequence, there are distinct groups of specialized acidophilic MOB where the variation
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between close relatives on a phylogeny is considerably small. The strong adaptation to pH could
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explain why the distribution of bacteria is driven by pH in the environment (Fierer & Jackson,
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2006). We would have liked to compare more properties, but they were not available for a
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representative set of species or were not continuous and thus not suited for the analysis
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performed.
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Constraints in the association of phylogeny and physiological traits
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We were particularly interested in the phylogenetic signal of physiological traits Km, and specific
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affinity, given their involvement in a key process of MOB; methane oxidation. These
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physiological traits had a stronger phylogenetic signal for the phylogeny based on the functional
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gene involved in methane oxidation (pmoA) than for the classical phylogenetic marker based on
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the housekeeping genes 16S rRNA. Similar findings were obtained in a study connecting
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microbial sulfate reduction rates to functional gene phylogeny (Jin et al., 2013). Phylogeny of
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the dsrA gene (dissimilatory sulfite reductase), a key enzyme of microbial sulfate reduction, was
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related stronger to sulfate reduction rates than phylogeny on the basis of 16S rRNA (Jin et al.,
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2013). Horizontal gene transfer (HGT) has been proposed to explain the absence of strong
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relationships between the 16S rRNA and dsrA gene phylogeny. While HGT is widely distributed
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(Retchless & Lawrence, 2010; Popa & Dagan, 2011; Polz et al., 2013), the strong congruency
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between the 16S rRNA and the pmoA gene phylogenies (Kolb et al., 2003; Op den Camp et al.,
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2009) support the idea that horizontal gene transfer is not very likely the dominant mechanism,
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and thus the ability to oxidize methane probably evolved from a common ancestor. The recently
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discovered pmoA gene sequences from Verrucomicrobia, which form a separate cluster
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compared to the proteobacterial homologues, show an early separation from proteobacterial
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MOB instead of a horizontal gene transfer (Dunfield et al., 2007). Hence, HGT cannot explain
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the observed differences in phylogenetic signals obtained in this study and are likely a result of
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phylogenetic resolution which was higher with the pmoA than with the 16S rRNA gene
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phylogeny (Fig. S1, S2). In addition, a long common ancestry with a complex methane metabolic
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pathway cannot easily evolve by HGT of a few genes (Tamas et al., 2014) which promoted that
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some of these analyzed physiological traits (e.g. Km apparent) might be more strongly associated
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with the protein function encoded by the pmoA gene.
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Some strains of MOB (e.g. Methylocystis sp. strain SC2) have two separate pMMO isozymes
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with different methane oxidation kinetics (Baani & Liesack, 2008) while others do not (e.g.
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Methylosinus trichosporium OB3b). Hence, the kinetic data used in this study may belong to
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kinetics of the different isozymes resulting in low phylogenetic signals. We analyzed pmoA
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genes encoding a subunit of the methane monooxygenase expressed at high methane mixing
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ratios (>600 ppmv) and the kinetic data used were obtained from cultures grown at methane
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mixing ratio above 4000 ppmv (Knief & Dunfield, 2005). Hence, although without expression
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profiles we cannot show that only that enzyme was expressed, these conditions suggest it was. In
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addition, the presence of pmoA enzyme paralogs could be an important – but not well
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characterized – factor in explaining low phylogenetic signal. Similarly, alternative enzymes such
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as the soluble methane monooxygenase (sMMO) can play a role as well. However, this enzyme
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is mainly expressed at a low copper concentration which was not limiting in the media used to
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obtain kinetic trait data (Knief & Dunfield, 2005).
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Although low phylogenetic signals may be a more general phenomenon, the mechanisms behind
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these signals can be fundamentally different. We suggest that functional analyses based on
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phylogenetic information, when not using information on the microbial traits directly, should be
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continued on the genes of the key enzymes involved rather than on 16S rRNA.
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The performance of phylogenetic signal measures
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It was not the purpose of this study to compare different measures of calculating phylogenetic
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signal. However, both Blomberg’s K and Abouheif’s Cmean tests did not show statistical support
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for phylogenetic signal in physiological traits related to methane oxidation.
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The Physiological traits in this study followed a BM model of evolution. It has been suggested
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that traits following a BM evolution require a large number of samples to test for presence of
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phylogenetic signal (Münkemüller et al., 2012). Hence, a low sample size could explain our
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observations from the randomization procedure in Blomberg’s K and to lesser extents for the
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Abouheif’s Cmean test statistic (which only was significant for the current small dataset for
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median K values around 0.5; Table S2). The confidence intervals, as derived from many
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phylogenies during the tree construction procedure in this study, however suggest the
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physiological traits related to methane oxidation have a phylogenetic signal, albeit weaker than
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that of general physiological traits.
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Conclusions
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Our study shows a weak phylogenetic signal for physiological traits in a key microbial group.
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Despite the recent rise in interest in the connection between evolution and ecosystem functioning
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(Cornelissen & Cornwell 2014) there are still not enough studies across different groups to
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determine if this is indicative of a general pattern (Pennell & Harmon, 2013) or if there are
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important difference between microbes on one hand and plants and animals on the other.
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However, this result is one important data point in a growing understanding of the evolution of
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the traits that affect ecosystems, across microbes, plants, and animals.
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Acknowledgements
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This study was part of the European Science Foundation EUROCORES Programme EuroEEFG
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and was financially supported by grants from the Netherlands Organization for Scientific
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Research (NWO) (Grant number 855.01.150). Many thanks to Dr. Levente Bodrossy from the
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CSIRO for Marine and Atmospheric Research for providing the pmoA sequence database. This
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publication is publication nr. XXXX of the Netherlands Institute of Ecology.
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Conflict of interest
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The authors declare no conflict of interest.
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488
Titles and legends to figures
489
Figure 1. Box plots of Blomberg’s K values of different physiological traits using maximum
490
likelihood (A, B) tree construction method for the pmoA gene (A) or 16S rRNA gene (B). 1Km
491
apparent in μM), 2specific affinity in l g-1 h-1, 3specific affinity in 10-12 l cell-1 h-1 (1-3, n=10), 4GC
492
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493
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494
Whiskers are 1.5 times the interquartile range of the data, and points outside this range are outliers.
495
Notches are displayed around each median and mark the 95% confidence interval for each median.
496
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497
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498
under Brownian motion.
499
24
500
Table 1. Major general physiological traits of MOB isolates used in this study
Isolate name
Methylomonas
methanica
Methylobacter
luteus
Methylobacter
psychrophilus
501
502
503
Type
Strain
Ia
n.a.
Ia
n.a.
Ia
Z-0021
pmoA/16S
accession
EU722434/
AF304196
not public/
X72772
AY945762/
AF152597
pHgrow*
pHopt*
5.5 - 9.0
7.2
5.0 - 9.0
7.0
5.5 - 9.0
7.2
T r*
20 37
20 37
3.5
- 10
Topt*
GC*
28.5
51.3
30.0
50.0
6.8
45.6
Reference
(Bowman et al.; 1993;
JGI, 2013)
(Wartiainen et al.,
2006a)
(Wartiainen et al.,
2006a; Dworkin et al.
2006)
(Wartiainen et al.,
2006a)
Methylobacter
AJ414658/
10 Ia
SV96
5.5 - 7.9
6.7
23.0
47.0
tundripaludum
AJ4146565
30
Methylosoma
DQ119047/
16 Ia
LC2
5.0 – 9.0 7
25.0
49.9 (Rahalkar et al., 2007)
difficile
DQ119050
30
Methylomicrobium
IR1
U31652.1/
20 (Bowman et al., 1993,
Ia
5.5
5.5
24.0
49
pelagica
pmoA
X72775
28
Garrity, 2005)
Methylosarcina
AMLAF177325/
22 Ia
5.0-9.0
7.0
29.5
54.1 (Wise et al., 2001)
fibrata
C10
AF177296
37
Methylosarcina
AY007286/
04 (Kalyuzhnaya et al.,
Ia
LW14
4.0-7.0
6.0
29.0
53.3
lacus
AY007296
35
2005)
Methylohalobius
AJ581836/
15 Ia
10Ki
6.5 - 7.5
7.0
30.0
58.7 (Heyer et al., 2005)
crimeensis
AJ581837
42
Methylothermus
AY829010/
37 Ia
MYHT
6.5 - 7.5
6.8
58.0
62.5 (Tsubota et al., 2005)
thermalis
AY829009
67
Methylothermus
AB536748/
37 Ia
HTM 55
5.2 - 7.5
6.0
57.5
54.4 (Hirayama et al., 2011)
subterraneus
AB536747
65
Methylomicrobium
AF307139/
6.8 –
08 (Kalyuzhnaya et al.,
Ia
5B
8.0
30.0
50.0
buryatense
AF307138
10.5
37
2008)
Methylococcus
Bath
L40804/
30 Ib
6.0 - 8.0
7.0
39.5
65.0 (Trotsenko et al., 2009)
capsulatus
(Texas)
AE017282
55
Methylocaldum
VKM14
U89301/
20 (Tsubota et al., 2005;
Ib
60- 8.0.
7.0
33.5
59.0
gracile
L
U89298
47
Trotsenko et al., 2009)
Methylocaldum
U89303/
37 Ib
OR2
6.0 - 8.5
7.2
55.0
56.5 (Trotsenko et al., 2009)
szegediense
U89300
62
Methylocapsa
AJ278727/
10 II
B2
4.2 - 7.2
5.7
20.0
63.1 (Dedysh et al., 2002)
acidiphila
AJ278726
30
Methylocystis
AJ414657/
05 (Wartiainen et al.,
II
SV97
5.0 – 9.0 7.0
27.0
62
rosea
AJ414656
37
2006b)
Methylocystis
AM283546/
05 II
H2
4.4 - 7.5
6.0
25.0
61.5 (Dedysh et al., 2007)
heyerii
AM283543
30
Methylocystis
AF533665/
20 63.4 (Bowman et al., 1993;
II
n.a.
5.0 - 9.0
7.0
28.5
parvus
Y18945
37
.
del Cerro et al., 2012)
Methylosinus
U31650/
20 II
OB3b
5.5 - 9.0
7.2
28.5
55.0 (Bowman et al., 1993)
trichosporium
Y18947
37
Methylovulum
AB501285/
05 II
HT12
6.0-7.5
6.7
28.0
49.3 (Iguchi et al., 2011)
miyakonense
AB501287
34
Methylacidiphilum
EF591085/
40 (Op den Camp et al.,
Ver
SoIV
0.8 – 5.8 2.0
55.0
40.8
fumariolicum
EF591088
65
2009)
Methylacidiphilum
EU223859/
40 (Op den Camp et al.,
Ver
V4
1.0 – 6.0 2.0
60.0
45.5
infernorum
EU223931
60
2009)
*pHgrow: pH range for growth, pHopt: optimal pH value for growth, Tr: temperature range for growth in °C, T opt:
optimal temperature for growth in °C.
25
504
Table 2. Major physiological traits related to methane oxidation of MOB isolates used in this
505
study (adapted from Knief et al. 2005)
pmoA/16S
Speccell*
Specliter*
Kmapp*
accession
not public/
Methylobacter luteus
Ia
n.a.
22.5 ± 0.8
4.1
52 ± 2
X72772
Methylomicrobium
U31654/
Ia
BG8
9 ± 1.4
11 ± 2
4.7
album
EU144025
Methylococcus
Bath
L40804/
Ib
5 ± 0.25
9.3 ± 0.5
10.2
capsulatus
(Texas)
AE017282
Methylocapsa
AJ278727/
II
B2
12 ± 1.7
13 ± 2
3.4
acidiphila
AJ278726
AJ868404/
Methylocystis sp.
II
DWT
17 ± 1.3
69 ± 5
2.2
AJ868423
AJ868405/
Methylocystis sp.
II
L6
10.6 ± 0.5
24 ± 1
4.3
AJ868422
Y18443/
Methylocystis sp.
II
LR1
20 ± 1.9
77 ± 7
2.5
Y18442
AJ431386/
Methylocystis sp.
II
SC2
34 ± 1.8
106 ± 6
3.2
AJ431384
Methylosinus
AJ868409/
II
BF1
31 ± 2.6
31 ± 2
5.6
trichosporium
AJ868424
Methylosinus
U31650/
II
OB3b
32 ± 1.2
46 ± 2
3.5
trichosporium
Y18947
*Speccell: specific affinity per cell (10-12 l cell-1 h-1), Specliter: specific affinity per liter (l g-1 h-1), Kmapp: Michaelis
constant Km (apparent) (μM)
Isolate name
506
507
508
Type
Strain
26
509
Supplemental material
510
Figure S1: Phylogenetic tree based on partial pmoA sequences.
511
512
Figure S2: Phylogenetic tree based on 16S rRNA sequences.
513
514
Table S1: P-values of tip shuffling randomization to test the presence of phylogenetic signal in
515
continuous-valued traits.
516
517
Table S2: The phylogenetic autocorrelation in physiological traits of MOB using the test of
518
Abouheif (1999).
27