Phylogenetic position of Placozoa based on large subunit (LSU) and small subunit (SSU) rRNA genes

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Abstract

The phylum Placozoa, represented by its single species Trichoplax adhaerens, has always been considered one of the most basal animal clades due to its low morphological complexity. However, despite its importance, the phylogenetic position of the Placozoa remains uncertain, as does the relationships between the basal metazoans. We sequenced the nearly complete large subunit (LSU) of the T. adhaerens rRNA gene together with the small subunit (SSU) rRNA and used several methods to infer the phylogenetic position of Placozoa and the relationships between the basal animal phyla. The phylogenetic trees obtained from the LSU, SSU and SSU + LSU datasets support monophyly of all basal metazoan phyla, except the Porifera. However, the relationships between all these phyla were not consistently resolved due to a strong conflict between the LSU and SSU phylogenetic signals. The hypotheses that Placozoa is a derived Cnidaria or the most basal metazoan were not supported by our data. Moreover, our analyses suggests that Placozoa is most likely the sister group of Cnidaria and/or Bilateria.

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A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.

Stéphane Guindon, Olivier Gascuel, Bruce Rannala (2003)

The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/.