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Expression and phylogenetic analysis of the zic gene family in the evolution and development of metazoans

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      Backgroundzic genes are members of the gli/glis/nkl/zic super-family of C2H2 zinc finger (ZF) transcription factors. Homologs of the zic family have been implicated in patterning neural and mesodermal tissues in bilaterians. Prior to this study, the origin of the metazoan zic gene family was unknown and expression of zic gene homologs during the development of early branching metazoans had not been investigated.ResultsPhylogenetic analyses of novel zic candidate genes identified a definitive zic homolog in the placozoan Trichoplax adhaerens, two gli/glis/nkl-like genes in the ctenophore Mnemiopsis leidyi, confirmed the presence of three gli/glis/nkl-like genes in Porifera, and confirmed the five previously identified zic genes in the cnidarian Nematostella vectensis. In the cnidarian N. vectensis, zic homologs are expressed in ectoderm and the gastrodermis (a bifunctional endomesoderm), in presumptive and developing tentacles, and in oral and sensory apical tuft ectoderm. The Capitella teleta zic homolog (Ct-zic) is detectable in a subset of the developing nervous system, the foregut, and the mesoderm associated with the segmentally repeated chaetae. Lastly, expression of gli and glis homologs in Mnemiopsis. leidyi is detected exclusively in neural cells in floor of the apical organ.ConclusionsBased on our analyses, we propose that the zic gene family arose in the common ancestor of the Placozoa, Cnidaria and Bilateria from a gli/glis/nkl-like gene and that both ZOC and ZF-NC domains evolved prior to cnidarian-bilaterian divergence. We also conclude that zic expression in neural ectoderm and developing neurons is pervasive throughout the Metazoa and likely evolved from neural expression of an ancestral gli/glis/nkl/zic gene. zic expression in bilaterian mesoderm may be related to the expression in the gastrodermis of a cnidarian-bilaterian common ancestor.

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      We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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        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:
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          MrBayes 3: Bayesian phylogenetic inference under mixed models.

          MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types-e.g. morphological, nucleotide, and protein-and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters.

            Author and article information

            [1 ]Pacific Biosciences Research Center, Kewalo Marine Laboratory, University of Hawaii, Manoa, 41 Ahui St Honolulu, HI 96813, USA
            BioMed Central
            5 November 2010
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            : 12
            Copyright ©2010 Layden et al; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


            Developmental biology


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