TreeBASE Search-Analyses for study 14910

@ARTICLE{TreeBASE2Ref22543, author = {Joel L Sachs and Ryan G Skophammer and Nidhanjali Bansal and Jason E Stajich}, title = {Evolutionary origins and diversification of proteobacterial mutualists}, year = {2014}, keywords = {evolutionary transition, mutualism, parasitism, symbiont}, doi = {10.1098/rspb.2013.2146}, url = {http://rspb.royalsocietypublishing.org/content/281/1775/20132146}, pmid = {24285193}, journal = {Proceedings of the Royal Society B: Biological Sciences}, volume = {281}, number = {1775}, pages = {}, abstract = {Mutualistic bacteria infect most eukaryotic species in nearly every biome. Nonetheless, two dilemmas remain unresolved about bacterial-eukaryote mutualisms: how do mutualist phenotypes originate in bacterial lineages and to what degree do mutualists traits drive or hinder bacterial diversification? Here, we reconstructed the phylogeny of the hyperdiverse phylum proteobacteria and investigated the origins and evolutionary diversification of mutualistic bacterial phenotypes compared to other lifestyles. We categorized host-association phenotypes using an information gathering heuristic that incorporates ambiguous information from a manually curated database. Our ancestral state reconstructions inferred a range of 34-39 independent origins of mutualist phenotypes in proteobacteria, revealing the surprising frequency with which host-beneficial traits have evolved in this phylum. We found proteobacterial mutualists to be more often derived from parasitic than from free-living ancestors, consistent with the untested paradigm that bacterial mutualists most often evolve from pathogens. We inferred that mutualists exhibit a negative net diversification rate (speciation minus extinction), which suggests that mutualism evolves primarily via transitions from other states rather than diversification within mutualist taxa. Moreover, our ancestral state reconstructions infer that proteobacterial mutualist lineages exhibit a paucity of reversals to parasitism or to free-living status. This evolutionary conservatism of mutualism is contrary to longstanding theory, which predicts that selection should often favor mutants in microbial mutualist populations that exploit or abandon more slowly evolving eukaryotic hosts. } }

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Analyses for Study 14910

Citation title: "Evolutionary origins and diversification of proteobacterial mutualists".

Study name: "Evolutionary origins and diversification of proteobacterial mutualists".

This study is part of submission 14910 (Status: Published).

Analyses

Analysis 1

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MrBayes --

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Protein sequences for phylogenetic reconstruction were selected based on conservation and lack of horizontal transfer among taxa. The following gene sequences were selected: dnaG, frr, gcp, infC, leuS, nusA, pgk, pheS, pyrG, rplA, rplB, rplC, rplD, rplE, rplF, rplK, rplL, rplM, rplN, rplO, rplP, rplR, rplS, rplT, rplV, rpmA, rpoA, rpoB, rpsB, rpsC, rpsD, rpsE, rpsG, rpsH, rpsI, rpsJ, rpsK, rpsL, rpsM, rpsO, rpsQ, rpsS, secY, serS, smpB, tsf, and ychF. Orthologs were identified through complementary methods. First, we searched for proteins annotated in the Kyoto Encyclopedia of Genes and Genomes Orthology database (http://www.genome.jp/kegg/ko.html). For organisms in our study that did not have entries in the entire KEGG database (e.g. multiple strains of the same species), we identified orthologs through searches of the NCBI Protein database. These searches were supplemented with reciprocal BLASTs, using proteins sequences from closely related organisms as the initial queries. This was intended to distinguish between multiple annotations of the same gene in an organism and also to confirm the orthologous relationship of the proteins. Orthologous sequences were downloaded from the Batch-Entrez website and aligned using default settings on MUSCLE. Alignments were concatenated using the BioPerl script concat_aln and were trimmed with the program trimAl (40) using the "strict" setting, resulting in a concatenation of 7,828 amino acids. Missing proteins were represented by gaps. MCMC phylogenies were reconstructed with MrBayes 3.2-cvs using a fixed rate model of evolution selected by an MCMC sampler that explored multiple models. Three MCMC runs of 106 generations each converged on a stationary distribution (average standard deviation of split frequencies < 0.01). One tree out of 100 for each of the final 24,100 generations (post-burnin) was sampled in each run and a majority-rules consensus tree was generated from this pool of 723 trees.

Output data

Analyses for Study 14910

Analyses

Analysis 1

1 Input object(s)

MrBayes --

1 Output object(s)