Hgc

- Chloroflexus

Dehalococcoides

Proteo HTU LGC HTU

Cyano HTU Chlorobium

-1000 changes

1000 changes

Figure 4. Trees generated by HTU analyses using MP showing the two related topologies observed.

lengths and improve the ability to resolve deep-branching relationships. In all HTU analyses, Proteobacteria, LGC, Cyanobacteria, and Chlorobium formed a monophyletic group (Fig. 4). However this monophyletic group had two alternative rootings, resulting in topologies I and II. Most HTU analyses using different taxa and sets of genes, produced topology I (this same topology was also seen in Figs. 2b and 3b).

With the character sets, taxa sets, and methods of analysis employed, Topology I (Fig. 4) appears to be leaning toward a "core" bacterial tree. This core is hypothesized to reflect the shared evolutionary history of highly conserved, essential proteins involved in the construction of the bacterial cell. Note, however, that initial global analyses did not immediately reveal this topology. It required identification and removal of taxa with long branches that were shown to exhibit LBA behavior. It also required choosing the most highly conserved genes - reinforcing the need for slowly evolving characters to resolve deep-branching relationships. Whether this topology is the true "core" topology for the bacterial domain or not will require analyses with additional taxa that not only adequately sample evolutionary diversity in the bacterial domain, but that also break up long branches that contribute to phylogenetic artifacts. Independent confirmation may also require analyses of an overlapping or different set of highly conserved genes.

4. Ancestral State Reconstruction of Prokaryotic Traits

Methodologies for ancestral state reconstruction (ASR) are some of the most contentious in systematic biology. Nevertheless, ASR can be useful in generating hypotheses regarding the evolution of traits - metabolic, ecological, or morphological - in important clades of interest (Maddison and Maddison, 2005). Such evolutionary hypotheses are ultimately testable: by constructing phylogenies of the genes underlying traits and by assessing particular time intervals in the rock record for biosignatures, lipids, and/or microfossils. For microorganisms, ASR can help to determine which traits are ancestral, and which traits are derived (Blank, 2004; Sanchez-Baracaldo et al., 2005).

For example, Fig. 5 shows the distribution aerobic respiration in the Euryarchaeota and the Crenarchaeota (Blank, 2009a). Aerobic respiration does not appear to be an ancient trait in these groups, rather anaerobic respiration appears to be basal in the archaeal domain (Woese, 1987; contra Castresana and Saraste, 1995). Aerobic respiration can be traced to the ancestor of five clades: (1) the Thermoplasmatales, (2) halophilic archaea, (3) Sulfolobales, (4) Thermoproteus neutrophilus-Pyrobaculum spp., and (5) in either the ancestor to Caldivirga-Thermocladium or the ancestor to the Thermoproteales. Genomes in the Thermoproteus neutrophilus and Pyrobaculum spp. were shown to have both cytochrome oxidases and quinol oxidases, and therefore the ancestor to this group was inferred to have obtained both of these genes, and therefore likely lived in an environment with a local presence of oxygen. Phylogenetic analyses showed that these genes were closely rere-lated and share a common ancestor, confirming this hypothesis. Thermocladium and Caldivirga are sister taxa capable of microaerophilic aerobic respiration and the Caldivirga genome contains a quinol oxidase. Thermofilum was formally described as an obligate anaerobe (Zillig and Reysenbach, 2001), however its genome also contains a quinol oxidase. Phylogenetic analysis (not shown; Blank, 2009a) shows that these two genes are closely related and shared a common ancestor. This suggests that the ancestor to Thermofilum-Caldivirga-Thermocladium, i.e., the ancestor to the Thermoproteales, was aerobic.

Euryarchaeota, MP ASR

Crenarchaeota, MP ASR

Oxygen as terminal electron acceptor

- equivocal

Euryarchaeota, MP ASR

Oxygen as terminal electron acceptor

- equivocal

Figure 5. ASR of the character aerobic respiration on the two major lineages of cultured Archaea, the Euryarchaeota (left) and the Crenarchaeota (right).

Crenarchaeota, MP ASR

Figure 5. ASR of the character aerobic respiration on the two major lineages of cultured Archaea, the Euryarchaeota (left) and the Crenarchaeota (right).

ASR starts with an input tree, for example a "core" tree developed using genome data. Next, traits are coded into characters and character states (these can be discrete unordered characters such as presence/absence of aerobic metabolism, discrete ordered characters such as freshwater/brackish/marine/hypersaline, or quantitative characters such as a optimal growth temperature). This is readily accomplished using programs such as MacClade (Maddison and Maddison, 2005) or Mesquite (Maddison and Maddison, 2006). Lastly, ancestral states are inferred given the tree, the character matrix, and the optimality criterion (parsimony, likelihood, and Bayesian). There is contentious debate as to the appropriate choice opti-mality criterion (e.g., Cunningham et al., 1998). Parsimony is frequently used given its simplicity, but does not take into consideration branch length and performs best when the rates of character change are low (Schluter et al., 1997). Likelihood or Bayesian methods are sometimes preferable, particularly since the error in state reconstructions can be readily estimated. Nevertheless, these methods are prone to estimation errors in branch lengths, and there are some types of characters (such as ecological characters) where parsimony may be a preferred criterion (Hardy, 2006).

There are a number of critical requirements for the accurate reconstruction of ancestral character states (Hardy, 2006). These include (1) a resolved starting tree, (2) an accurate tree, (3) dense taxonomic sampling, (4) adequate knowledge of the presence or absence of the traits of interest, and (5) choice of optimality criterion. ASR can be sensitive to the presence of polytomies, or collapsed branches, in the tree because phylogenetic reconstruction algorithms treat them as unresolved ("soft" polytomies), whereas in ASR they are often treated as multiple speciation events ("hard" polytomies; Maddison and Maddison, 2005). Thus, it is desirable to either resolve the polytomy if possible (by excluding or including taxa or characters) or to perform ASR on all possible trees underlying the polytomy. Because the inference of ancestral character states uses the relationships (and often the branch lengths) of the tree in the reconstruction, an accurate tree is essential. Ideally for prokaryotes this would be a supermatrix tree thought to be the "core" tree of the group of interest.

Dense taxonomic sampling is also essential for accurate ASR (Salisbury and Kim, 2001). There are two fundamental reasons for this: it produces a better starting phylogeny and it better samples the diversity of traits within the group. Although the number of genome sequences is increasing rapidly, few are available for most groups of free-living prokaryotes. ASR studies, therefore, must rely heavily on trees constructed with single genes (most often 16S rDNA) that have much less resolving power than multiple genes. The study of character evolution in the Cyanobacteria exemplifies this problem (Sanchez-Baracaldo et al., 2005). Here, a supermatrix of 36 genes from 14 genomes was used to calculate a core cyanobac-terial tree. To better sample the broad diversity of taxa in the Cyanobacteria, 71 additional taxa were added to the core tree using single gene data (all taxa had 16S rDNA; 34 had rpoC). Because 16S and rpoC contain limited phylogenetic signal, the final tree had many collapsed branches (although it was significantly better resolved than previous studies). Thus, prokaryote ASR studies must strike a balance between increasing taxon sampling and decreasing phylogenetic resolution. In the Cyanobacteria study, ancestral character states were inferred using Fitch parsimony in MacClade (Maddison and Maddison, 2005), implementing the "soft" polytomies option. Populations of sub-trees with no polytomies have also been examined using a broader diversity of optimality criteria (Blank and Sanchez-Baracaldo, in preparation). However, these sub-trees perforce contained much fewer taxa.

Inadequate knowledge of prokaryotic traits may be another significant issue in the reconstruction of ancestral character states. For one, cultures can lose traits over time. In the Cyanobacteria, cultures have been shown to lose important traits such as heterocysts, hormogonia, akinetes, and sheaths (Wilmotte, 1994; Hoiczyk, 1998; Meeks et al., 2002). Morphologies of cells and colonies have been shown to change in cultures of Nostoc, Anabaena, and Aphanizomenon (Gugger et al., 2002; Meeks et al., 2002). It is also widely appreciated that microbial cultures undergo mutational, morphological, and physiological changes while in culture (e.g., Riley et al., 2001). Such changes could potentially inflate the number of inferred character gains and/or losses and result in incorrect estimation of the ancestral state.

Inadequate knowledge of the traits of prokaryotes may also be an issue in ASR. When new microbial taxa are described, the cultures are tested for optimal growth temperature, the ability to use a range of electron acceptors and donors, and whether it can grow aerobically or anaerobically. Characterizations may also report optimal salinity, pH, and any unique traits. Yet a handful of recent papers have shown that sometimes we know little about the physiology of even "well-understood" microorganisms. One surprise was that many thermophiles, even the heterotroph Thermotoga and several methanogens, were found to be able to reduce iron (Vargas et al., 1998; Bond and Lovley, 2002). Another surprise was that many "strict anerobes", including some sulfate-reducers, iron reducers, and a Grampositive acetogen, can not only tolerate but also reduce oxygen (Lemos et al., 2001; Baughn and Malamy, 2004; Lin et al., 2004; Das et al., 2005). This illustrates that our knowledge of the traits of cultured prokaryotic taxa is imperfect. Such an underestimation of traits could lead to an over-estimation of the numbers of gains and losses of the traits and an incorrect inference of the ancestral state.

Lastly, our knowledge of microbial biodiversity is incomplete. It is well known that over 99% of microbial taxa are uncultured, either because they have yet to be cultured, do not grow in isolation, or are "unculturable" (Amann et al., 1995). The process of culturing itself selects for strains that grow well under artificial laboratory conditions and these strains are likely not dominant members of the natural community. Also, there is no doubt that significant extinction has occurred in the microbial world over the last 4 billion years. Thus, the traits of most extant uncultured and extinct microbial lineages are fundamentally unknowable.

Given our incomplete knowledge of microbial traits and the uncertainties in the ASR itself, it is of paramount importance that any hypotheses of character evolution be tested. For metabolic traits, this can be relatively straight forward by constructing phylogenetic trees of the genes underlying the traits. If no statistical incongruence is found with the character reconstruction, confidence can be placed on the ASR. Such tests require the availability of the appropriate gene sequences, and preferably whole genomes. Lastly, the hypotheses of character evolution should be tested by analyzing the rock record at various age intervals for isotopic biomarkers, trace element biomarkers, lipids, and/or microfossils that might either support or refute the predicted transitions in character states.

5. Phylogenomic Dating

This last section discusses how ASR can be used to identify age constraints in major microbial groups, focusing on a recent character study of the archaeal domain of life (Blank, 2009a, b). Here, a supermatrix of 38 proteins, the SSU rRNA, and the LSU rRNA gene was constructed using 20 archaeal genome sequences. Analyses were performed on the entire matrix, on functional categories of genes, and on individual genes using parsimony, distance, likelihood, and Bayesian methods. As discussed above, the placement of Methanopyrus was problematic, although most trees showed it to be the basal branch in the Euryarchaeota.

Given only 16 euryarchaeal and 4 crenarchaeal genomes, a number of additional lineages were added using 16S rDNA to adequately sample taxo-nomic diversity. Weakly supported branches were collapsed into polytomies. Next, a number of metabolic, habitat, and physiological traits were coded into characters using MacClade, and the ancestral character states inferred using maximum parsimony.

Five candidate clades were inferred to have aerobic ancestors (Fig. 5). This was supported by BLAST results, showing that these clades had terminal oxidases most similar to other taxa in the clade. Next, the inferred habitats of the ancestor were examined. In the Sulfolobales, Thermoplasmatales, and Thermoproteus neutrophilus-Pyrobaculum spp. the inferred ancestors lived in habitats that were too hot and/or too acidic for Cyanobacteria (a local source of O2) to exist. Therefore these three clades must have originated after 2.32 Ga, when oxygen appeared in the atmosphere (Bekker et al., 2004; darker arrows in Fig. 6). The halophilic Archaea, in contrast, have niches that overlap with Cyanobacteria (although halophilic Cyanobacteria branch peripherally in the tree; Garcia-Pichel, 1998; Sanchez-Baracaldo et al., 2005). Thus, it is possible, although not certain, that the halophilic archaea arose after 2.32 Ga (lighter arrow in Fig. 6). Lastly, because Thermofilum, Caldivirga, and Thermocladium are all extreme ther-mophiles, their aerobic ancestor must have originated before 2.32 Ga.

These "oxygen age constraints" provide valuable information about the antiquity of additional traits in the archaeal domain. Many traits were inferred to be ancient: sulfur reduction, hydrogenotrophic methanogenesis, autotrophy, heterotrophy, and hyperthermophily. Many traits were inferred to have arisen after atmospheric oxygenation: aerobic respiration, nitrate reduction, hydrogen

Euryarchaeota, MP ASR Crenarchaeota, MP ASR

Euryarchaeota, MP ASR Crenarchaeota, MP ASR

Figure 6. Age constraints placed upon the Euryarchaeota and Crenarchaeota trees. The darker arrows point to clades where the confidence in the age constraint is higher.

oxidation, sulfate and thiosulfate reduction, sulfide oxidation, and hyperacidophily. Traits that were not found to be ancient: mesophilic methanogenesis, anaerobic methane oxidation, thermophily and mesophily in the Crenarchaeota, and halophily.

As outlined above, the accuracy of these hypotheses depend upon the accuracy of the starting tree. The starting tree contains several polytomies given the inclusion of rRNA sequences. With more genomes, the resolution of this tree should improve and so should the ASR. Next, the hypotheses depend upon taxo-nomic sampling. As more genome sequences become available, future studies will include a better sampling of taxonomic diversity without compromising the resolution of the tree. The hypotheses also depend on our imperfect knowledge of traits. As our knowledge of prokaryote physiology improves, so should ASR studies. Lastly, as more genome sequences are completed our knowledge of the diversity of traits increases as we identify the presence or absence of genes that underlie the traits.

At present, there is scant evidence regarding the antiquity of Archaea in the geologic record. Archaeal microfossils lack unique morphological synapomor-phies, and are indistinguishable from most bacterial microfossils. Lipid biomarker studies suggest archaeal lipids are absent in the Archean era (Brocks et al., 2003), but present in later Mesoproterozoic rocks (Logan et al., 2001; Dutkiewicz et al., 2003; Li et al., 2003). These lipids, however, are in most archaeal taxa and so provide little information on the age of specific archaeal taxa. Lastly, carbon isotopic fractionation at 2.7 Ga suggests the presence of methanogens (and meth-anotrophs; Hayes et al., 1992). Yet methanogens are widespread in the Euryarchaeota, so this also provides little age information. In contrast, phyloge-nomic dating provides new insights on the antiquity of many archaeal metabolic processes. Ultimately, phylogenomic dating may provide a novel way to determine the age of many microbial processes and to reconstruct ancient biogeochemical cycles. At the same, given the difficulties associated with prokaryote phylogenetics and ancestral state reconstruction, the hypotheses that are generated using phyl-ogenomic dating must be carefully tested. Ground-truthing with the rock record will be essential.

6. Acknowledgements

Thanks to Brent Mishler for assistance in implementing compartmentalization with the bacterial supermatrix and Patricia Sanchez-Baracaldo for assistance in the character study of the Cyanobacteria. Funding sources include: NSF PEET grant DEB-9712347, NERC grant NER/T/S/2000/01356, and NASA Exobiology grants NNG04GM47G and NNG04GJ84G.

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Biodata of Roberto Barbieri and Barbara Cavalazzi of "Fossil Microorganisms at Methane Seeps: An Astrobiological Perspective"

Roberto Barbieri is professor of Paleontology at the University of Bologna. As a palaeontologist and geomicrobiologist he investigates modern and ancient ecosystems from stressful conditions and the way for their reconstruction in rock deposits. Presently, he is investigating the role of the microbial communities of cold seep ecosystems and non-marine evaporites as terrestrial analogues of Martian environments.

E-mail: [email protected]

Barbara Cavalazzi is currently a research fellow at the University of Bologna. She obtained her Ph.D. (Paleontology) from the University of Modena and Reggio Emilia in 2005. Barbara's scientific interests include geomicrobiology and the micropaleontology of hydrothermal/cold seep-derived deposits, and other rock records from extreme environments, and their astrobiological implications.

E-mail: [email protected]

Roberto Barbieri Barbara Cavalazzi

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