Dataset Information

Inadvertent Paralog Inclusion Drives Artifactual Topologies and Timetree Estimates in Phylogenomics.

ABSTRACT: Increasingly, large phylogenomic data sets include transcriptomic data from nonmodel organisms. This not only has allowed controversial and unexplored evolutionary relationships in the tree of life to be addressed but also increases the risk of inadvertent inclusion of paralogs in the analysis. Although this may be expected to result in decreased phylogenetic support, it is not clear if it could also drive highly supported artifactual relationships. Many groups, including the hyperdiverse Lissamphibia, are especially susceptible to these issues due to ancient gene duplication events and small numbers of sequenced genomes and because transcriptomes are increasingly applied to resolve historically conflicting taxonomic hypotheses. We tested the potential impact of paralog inclusion on the topologies and timetree estimates of the Lissamphibia using published and de novo sequencing data including 18 amphibian species, from which 2,656 single-copy gene families were identified. A novel paralog filtering approach resulted in four differently curated data sets, which were used for phylogenetic reconstructions using Bayesian inference, maximum likelihood, and quartet-based supertrees. We found that paralogs drive strongly supported conflicting hypotheses within the Lissamphibia (Batrachia and Procera) and older divergence time estimates even within groups where no variation in topology was observed. All investigated methods, except Bayesian inference with the CAT-GTR model, were found to be sensitive to paralogs, but with filtering convergence to the same answer (Batrachia) was observed. This is the first large-scale study to address the impact of orthology selection using transcriptomic data and emphasizes the importance of quality over quantity particularly for understanding relationships of poorly sampled taxa.

SUBMITTER: Siu-Ting K

PROVIDER: S-EPMC6526904 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Inadvertent Paralog Inclusion Drives Artifactual Topologies and Timetree Estimates in Phylogenomics.

Siu-Ting Karen K Torres-Sánchez María M San Mauro Diego D Wilcockson David D Wilkinson Mark M Pisani Davide D O'Connell Mary J MJ Creevey Christopher J CJ

Molecular biology and evolution 20190601 6

Increasingly, large phylogenomic data sets include transcriptomic data from nonmodel organisms. This not only has allowed controversial and unexplored evolutionary relationships in the tree of life to be addressed but also increases the risk of inadvertent inclusion of paralogs in the analysis. Although this may be expected to result in decreased phylogenetic support, it is not clear if it could also drive highly supported artifactual relationships. Many groups, including the hyperdiverse Lissam ...[more]

PMID: 30903171

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Project description:BACKGROUND:The CEA gene family is one of the most rapidly evolving gene families in the human genome. The founder gene of the family is thought to be an ancestor of the inhibitory immune checkpoint molecule CEACAM1. Comprehensive analyses of mammalian genomes showed that the CEA gene family is subject to tremendous gene family expansion and contraction events in different mammalian species. While in some species (e.g. rabbits) less than three CEACAM1 related genes exist, were in others (certain microbat species) up to 100 CEACAM1 paralogs identified. We have recently reported that the horse has also an extended CEA gene family. Since mechanisms of gene family expansion and diversification are not well understood we aimed to analyze the equine CEA gene family in detail. RESULTS:We found that the equine CEA gene family contains 17 functional CEACAM1-related genes. Nine of them were secreted molecules and eight CEACAMs contain transmembrane and cytoplasmic domain exons, the latter being in the focus of the present report. Only one (CEACAM41) gene has exons coding for activating signaling motifs all other CEACAM1 paralogs contain cytoplasmic exons similar to that of the inhibitory receptor CEACAM1. However, cloning of cDNAs showed that only one CEACAM1 paralog contain functional immunoreceptor tyrosine-based inhibitory motifs in its cytoplasmic tail. Three receptors have acquired a stop codon in the transmembrane domain and two have lost their inhibitory motifs due to alternative splicing events. In addition, alternative splicing eliminated the transmembrane exon sequence of the putative activating receptor, rendering it to a secreted molecule. Transfection of eukaryotic cells with FLAG-tagged alternatively spliced CEACAMs indicates that they can be expressed in vivo. Thus detection of CEACAM41 mRNA in activated PBMC suggests that CEACAM41 is secreted by lymphoid cells upon activation. CONCLUSIONS:The results of our study demonstrate that alternative splicing after gene duplication is a potent mechanism to accelerate functional diversification of the equine CEA gene family members. This potent mechanism has created novel CEACAM receptors with unique signaling capacities and secreted CEACAMs which potentially enables equine lymphoid cells to control distantly located immune cells.

Project description:To explore whether systematic reviewers selectively include trial effect estimates in meta-analyses when multiple are available, and what impact this may have on meta-analytic effects.Cross-sectional study.We randomly selected systematic reviews of interventions from 2 clinical specialties published between January 2010 and 2012. The first presented meta-analysis of a continuous outcome in each review was selected (index meta-analysis), and all trial effect estimates that were eligible for inclusion in the meta-analysis (eg, from multiple scales or time points) were extracted from trial reports.We calculated a statistic (the Potential Bias Index (PBI)) to quantify and test for evidence of selective inclusion. The PBI ranges from 0 to 1; values above or below 0.5 are suggestive of selective inclusion of effect estimates more or less favourable to the intervention, respectively. The impact of any potential selective inclusion was investigated by comparing the index meta-analytic standardised mean difference (SMD) to the median of a randomly constructed distribution of meta-analytic SMDs (representing the meta-analytic SMD expected when there is no selective inclusion).31 reviews (250 trials) were included. The estimated PBI was 0.57 (95% CI 0.50 to 0.63), suggesting that trial effect estimates that were more favourable to the intervention were included in meta-analyses slightly more often than expected under a process consistent with random selection; however, the 95% CI included the null hypothesis of no selective inclusion. Any potential selective inclusion did not have an important impact on the meta-analytic effects.There was no clear evidence that selective inclusion of trial effect estimates occurred in this sample of meta-analyses. Further research on selective inclusion in other clinical specialties is needed. To enable readers to assess the risk of selective inclusion bias, we recommend that systematic reviewers report the methods used to select effect estimates to include in meta-analyses.

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Inadvertent Paralog Inclusion Drives Artifactual Topologies and Timetree Estimates in Phylogenomics.

Publications

Inadvertent Paralog Inclusion Drives Artifactual Topologies and Timetree Estimates in Phylogenomics.

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