In 4 we described the analyses of several interdomain genome quartets. Some of the analyses were performed using a posterior probability mapping approach referred to as Maximum Likelihood (ML) mapping, a name that was coined in the original description of this approach 17. We will use this term throughout the manuscript. In ML mapping posterior probabilities are calculated from the maximum likelihood values (see 17 and 4 for the details). One noteworthy finding was that in the genome quartet including Synechocystis sp., Halobacterium sp., Aquifex aeolicus and Thermotoga maritima the grouping of Halobacterium sp. with Synechocystis sp. was recovered by many more QuartOPs than the grouping expected following 16S rRNA phylogeny (see Fig. 1A). Note that throughout the manuscript we refer to a particular tree by mentioning two species out of four (e.g., in this case grouping of Halobacterium sp. with Synechocystis sp.); however, the trees are unrooted and therefore grouping of the other two taxa is implied. To test if this association was specific for Synechocystis sp., we had repeated the analyses replacing Synechocystis sp. with Bacillus subtilis. The results were qualitatively the same (data not shown). To test for the possibility that LBA 6 might be the reason for the strong support of Halobacterium sp. grouping with Synechocystis sp., we had repeated the analyses replacing the Halobacterium sp. genome with that from Archaeoglobus fulgidus, another archaeon. The majority of QuartOPs supported the grouping of the thermophilic archaeon Archaeoglobus with the thermophilic bacteria Aquifex and Thermotoga (see Fig. 1B). In this study, we reanalyzed the above-mentioned genome quartets by adding homologous sequences from sixty reference genomes to each QuartOP creating what we call "extended datasets". The dataflow is depicted in Figure 2. For each extended dataset we obtained bootstrap support values for each of the three four-taxon "subtrees" and we plotted the bootstrap support values into barycentric coordinates. Throughout this manuscript we use a graph theory definition of a subtree, i.e. "A tree G' whose graph vertices and graph edges form subsets of the graph vertices and graph edges of a given tree G" 18. In particular, sequences (OTUs) included in the subtree are not required to be neighbors in the original tree. Subtrees defined according to these rules are different from subclades (see figure 2 for an illustration). For example, if the topology ((A,D),(B,C)) is supported by a given bootstrap sample, this means that in the tree calculated from this sample the sequence from genome A groups closer to the one from D than to the one from B or C (figure 2).

Maps of bootstrap support values calculated from the extended datasets are shown in figure 3. The results are similar to the analyses using ML mapping (see 4 and figure 1). At every level of support the plurality consensus groups the mesophilic archaeon with the mesophilic bacterium and the two thermophilic bacteria with one another (figure 1 and 3, panels A). The plurality support changes in favor of the archaeon – Aquifex grouping, when the genome of a mesophilic archaeon is replaced with that of an extremely thermophilic archaeon Archaeoglobus fulgidus (figure 1 and 3, panels B). The main difference between ML maps and bootstrap support maps from extended datasets is that the confidence values are much lower for the extended datasets evaluated with bootstrap. The cause for these lower support values is discussed below.

The relation between the different bacterial phyla, and the placement of the bacterial root remains uncertain (e.g., see 19). Therefore, it is not clear which of the three unrooted trees for the genome quartet in question represents the true organismal phylogeny of the four genomes. The lack of phylogenetic signal alone should result in QuartOPs that map to the center of the triangle. However, we observe many genes that prefer one topology to the other two. The genes in figures 1A and 3A that group the ortholog from Halobacterium with its putative ortholog from Synechocystis might do so for a variety of different reasons:

A) Horizontal gene transfer

   A₁) between a mesophilic bacterium and a mesophilic archaeon

   A₂) between the extremely mesophilic bacteria Aquifex and Thermotoga

B) Phylogenetic reconstruction artifacts

   B₁) due to long branch attraction

   B₂) due to compositional bias

   B₃) due to the lack of phylogenetic signal

C) Unrecognized paralogy

D) This grouping reflects organismal evolutionary history.

It is important to realize that any of the processes listed under A through C, alone or in combination, might result in well supported QuartOPs that group the halobacterial homolog with the cyanobacterial counterpart. Some of these possibilities can be distinguished in the individual phylogenies of the extended datasets. Table 1 summarizes these findings. There are many datasets which don't conform to the 16S rRNA based expectation in more than one respect: Aquifex and Thermotoga are recovered as sister groups, and either the cyanobacterial sequences groups within a cluster of Archaeal sequences, or the halobacterial sequence groups within a well-supported cluster of Bacterial homologs. We considered these unexpected phylogenies as supported, if the branch that separated this group from the other homologs had at least 70% bootstrap support (note: in this case we used the bootstrap support for individual branches, not the support for subtrees). At first sight this might appear as a rather low level of support; however, adding more sequences tends to shorten the internal branches and thus lowers their individual bootstrap support value (for discussion see 20). The support for the subtree topology (see figure 2) is larger than 90% for all entries. There is only one case where the observed subtree ((Aquifex, Thermotoga), (Halobacterium, Synechocystis)) appears to be due to unrecognized paralogy. In four instances a well-supported branch suggests an interdomain gene transfer and in 11 instances an exchange between Thermotoga and Aquifex. These findings apparently support the notion that genes are frequently transferred between divergent prokaryotes; however, Halobacterium has an amino acid composition that often deviates significantly from that of the other homologs. The instances where both the halobacterial sequence and its phylogenetic neighbor failed a test for homogeneous composition are indicated in table 1. This leaves eight well-supported instances of at least one horizontal gene transfer event out of the twelve datasets without shared compositional biases.

The analyses depicted in figure 3 and table 1 demonstrate that bootstrap support value mapping in general, and support value mapping using extended datasets in particular, are useful in screening for genes that were transferred between divergent organisms. Replacing the genome from a mesophilic archaeon with that from an extremely thermophilic one, changes the topology of the subtree that has plurality support. This observation is in agreement with the hypothesis that genes are more frequently shared between organisms that live in similar environments 21. However, given that the Halobacterium genome is renowned for its large number of genes with bacterial character 2223, the total number of genes identified in this study as putatively transferred between the mesophilic bacteria and the halobacteria is very small. There are several reasons for this observation. Useful phylogenetic information retained in molecular sequences is constantly overwritten by more recent substitution events. The more divergent the analyzed genomes are, the more QuartOPs will be undecided about the most supported topology. Furthermore, support value mapping can only identify gene transfers that resulted in orthologous replacement. Last but not least, the applied approach to assemble QuartOPs is overly restrictive. Lineage specific duplications result in two orthologs being present in a single genome. These genes are paralogs of one another, but both are orthologs to the gene present in the genomes that branch off before the lineage specific duplication 24. Despite these shortcomings, support value mapping, especially when using extended datasets, provides a quick method to appraise the extent of genomic mosaicism, to delineate preliminarily the major flows of genes in microbial evolution (plurality or majority consensus), and to find subsets of potentially transferred genes.

To assess the utility of probability and bootstrap support values mapping for detecting more recent interphylum gene transfer events, we calculated extended datasets for the genome quartet of Synechocystis sp., Chlorobium tepidum, Rhodobacter capsulatus and Rhodopseudomonas palustris (see figure 4 and 3). This genome quartet has a strong phylogenetic signal grouping together the two alpha proteobacteria R. capsulatus and R. palustris. This example had previously been utilized to demonstrate the validity of ML mapping 3 showing that the vast majority of QuartOPs group the proteins from the two more closely related organisms together. However, there are 14 QuartOPs that support the two alternative topologies with 99% posterior probability. These have to be regarded as candidates for horizontal gene transfer. Analysis of this genome quartet using extended datasets shows that some of these 14 QuartOPs are also supported by high bootstrap support values (above 90%). Figures 5 through 8 provide further analysis of the extended datasets for these QuartOPs. The cases of the cation-transporting ATPases (figure 5) and the hypothetical proteins depicted in figure 6 probably represent unrecognized paralogies. The proteins from R. palustris and R. capsulatus each group together with homologs from other alpha proteobacteria, and in some instances a single genome encodes both paralogs (Bradyrhizobium japonicum in case of hypothetical protein family and Sinorhizobium meliloti in case of the cation-transporting ATPases). It appears likely that R. palustris has lost one and R. capsulatus the other paralog. In these two instances the unexpected behavior of the QuartOPs is due to failure of the strategy to select orthologous genes. In contrast, the cases of the water channel protein family and the methionyl-tRNA synthetases are best explained by horizontal gene transfer. None of the reference genomes contains paralogs whose differential loss might explain the observed phylogenies (figures 7 and 8).

The higher frequency of unrecognized paralogs among the putatively horizontally transferred genes is due to the much larger number of QuartOPs analyzed. The detected number of unrecognized paralogs corresponds to less than 1% of the QuartOPs that contain sufficient phylogenetic information to support a topology with more than 90% bootstrap support (figure 4C). Every instance of unrecognized paralogy will result in a QuartOP deviating from the majority consensus revealed in this analysis.

It is difficult to compare posterior probabilities of QuartOPs directly with bootstrap support values of much larger datasets. Empirical studies 4 as well as the simulation studies 2526 indicate that bootstrap measures are much more conservative than Bayesian posterior probabilities. In the four-taxon cases analyzed in 4 a posterior probability of 0.99 calculated according to Strimmer and von Haeseler 17 was found to correspond to only 70% bootstrap support calculated from non-extended datasets.

To demonstrate that the observed drop in support is due to the more conservative nature of bootstrapping and not due to increased OTU sampling we re-analyzed the same genome quartets using ML mapping (figure 1 and figure 4A), bootstrap support values calculated from only the four aligned QuartOPs (Additional file: 2 and figure 4B), and bootstrap support values calculated from the extended datasets (figure 3 and figure 4C) as described in figure 2. Note that in case of ML mapping only posterior probabilities greater than 0.99 are counted as strong support (greater than 0.9 for moderate support), whereas in case of the bootstrap support value maps greater than 0.9 is classified as strong support (greater than 0.7 for moderate support). Table 2 summarizes the overall number of QuartOPs supporting different topologies with different measures of support. There is a dramatic drop in the number of QuartOPs with strong support from the 99% posterior probabilities to 90% bootstrap from non-extended datasets. However, the added accuracy obtained through increased OTU sampling does not change the support as radically as the shift from posterior probabilities to the bootstrap support measure. Apparently, the increased accuracy due to better OTU sampling on average increases the bootstrap support of a given subtree as often as it lowers the support value. The higher support values found for ML mapping are solely due to the less conservative nature of the calculated support measure.