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Confusion will be my epitaph: genome-scale discordance stifles phylogenetic resolution of Holothuroidea.
Mongiardino Koch N
,
Tilic E
,
Miller AK
,
Stiller J
,
Rouse GW
.
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Sea cucumbers (Holothuroidea) are a diverse clade of echinoderms found from intertidal waters to the bottom of the deepest oceanic trenches. Their reduced skeletons and limited number of phylogenetically informative traits have long obfuscated morphological classifications. Sanger-sequenced molecular datasets have also failed to constrain the position of major lineages. Noteworthy, topological uncertainty has hindered a resolution for Neoholothuriida, a highly diverse clade of Permo-Triassic age. We perform the first phylogenomic analysis of Holothuroidea, combining existing datasets with 13 novel transcriptomes. Using a highly curated dataset of 1100 orthologues, our efforts recapitulate previous results, struggling to resolve interrelationships among neoholothuriid clades. Three approaches to phylogenetic reconstruction (concatenation under both site-homogeneous and site-heterogeneous models, and coalescent-aware inference) result in alternative resolutions, all of which are recovered with strong support and across a range of datasets filtered for phylogenetic usefulness. We explore this intriguing result using gene-wise log-likelihood scores and attempt to correlate these with a large set of gene properties. While presenting novel ways of exploring and visualizing support for alternative trees, we are unable to discover significant predictors of topological preference, and our efforts fail to favour one topology. Neoholothuriid genomes seem to retain an amalgam of signals derived from multiple phylogenetic histories.
Figure 1. . Representative holothuroid diversity included in this study. (a). Synapta sp. (b). Peniagone cf. vitrea. (c). Benthogone sp. (d). Pseudocolochirus violaceus. (e). Abyssocucumis albatrossi. (f). Colochirus robustus. (g). Ypsilothuria n. sp. (SIO-BIC E6221). (h). Molpadia amorpha. (i). Pseudostichopus cf. mollis. (j). Synallactidae. (k). Bathyplotes cf. moseleyi. The classification of these terminals can be found in electronic supplementary material, table S1. All photos except (g) are of the voucher specimens sequenced (catalogue numbers can be found in electronic supplementary material, table S1; further sampling information is accessible through the SIO-BIC online database, https://sioapps.ucsd.edu/collections/bi/). Images (b), (c), (i) and (k) are courtesy of the Schmidt Ocean Institute, and image (j) is courtesy of Monterey Bay Aquarium and Research Institute.
Figure 2. . Summary of phylogenetic inference results. (a). Strict consensus of the nine inference conditions explored, varying both the number of loci and the method of inference. Nodes disagreeing between analyses are collapsed and labelled (see panels (b,c) for further details); branch lengths are otherwise taken from the CAT-PMSF analysis of the full dataset. (b). Monophyly of a clade composed of two cucumariid terminals, Colochirus robustus and Cucumaria georgiana, is rejected by ASTRAL-III, but upheld by the other methods (legend for support value grid is shown in (a)). (c). Systematic disagreement between all methods of inference regarding relationships among major neoholothuriid clades. The resolution favoured by each method is found across datasets of different sizes. Topologies, branch lengths and support values for each individual analysis are shown in electronic supplementary material, figures S2–S4.
Figure 3. . Exploration of support for alternative neoholothuriid topologies across loci. (a). Principal components (PC) axes obtained from the three ΔGLS. Percentages of explained variance are shown on axis labels. Loci are colour coded depending on their favoured topology. (b). Relationship between the PC axes and the scores of individual ΔGLS. Trendlines correspond to LOESS smoothing curves, and ρ-values (Spearman's rank correlation coefficients) are shown when absolute values > 0.7, taken to represent strong correlations. The area included within ± 2 log-likelihood units is highlighted and considered an area of weak support. Note the markedly different scales of the y-axes for PCs 1 and 2. Topologies are colour coded as in (a).
Figure 4. . Categorization of loci depending on their favoured topology, and exploration of potential determinants. Colouring scheme follows that of figure 3. (a). Most loci (615, 55.9% of the full dataset) can be considered uninformative regarding relationships among neoholothuriid clades. The remainder can be classified into those supporting a given topology (denoted using a plus sign, +) if they favour a given resolution against both alternatives (coloured section of bar chart) or only one (white section of bar chart) with a ΔGLS ≥ 2; or rejecting a given topology (denoted using a minus sign, −). The number of loci either supporting (right side of wheel) or rejecting (left side of wheel) the ASTRAL topology are comparable in number: 207 (18.8%) versus 278 (25.3%), respectively. Further details on loci categorization can be found in electronic supplementary material, figure S7. (b). Top: Exploration of 15 potential determinants of ΔGLS. Arrows indicate directions of maximum correlation between scores and determinants; their length is scaled to the strength of the correlation. Predictors mostly load onto PC 2. R2 and p-values are shown in electronic supplementary material, table S2, but no correlation is significant. Bottom: Smoothed surface of alignment length, the only significant determinant found using a classification tree. Longer loci are more likely to be informative, yet alignment length does not predict which topology is preferred (see electronic supplementary material, figure S9).
Al Jewari,
Conflict over the Eukaryote Root Resides in Strong Outliers, Mosaics and Missing Data Sensitivity of Site-Specific (CAT) Mixture Models.
2023, Pubmed
Al Jewari,
Conflict over the Eukaryote Root Resides in Strong Outliers, Mosaics and Missing Data Sensitivity of Site-Specific (CAT) Mixture Models.
2023,
Pubmed
Arcila,
Genome-wide interrogation advances resolution of recalcitrant groups in the tree of life.
2017,
Pubmed
Ballesteros,
Comprehensive Species Sampling and Sophisticated Algorithmic Approaches Refute the Monophyly of Arachnida.
2022,
Pubmed
Ballesteros,
A Critical Appraisal of the Placement of Xiphosura (Chelicerata) with Account of Known Sources of Phylogenetic Error.
2019,
Pubmed
Bolger,
Trimmomatic: a flexible trimmer for Illumina sequence data.
2014,
Pubmed
Burbrink,
Interrogating Genomic-Scale Data for Squamata (Lizards, Snakes, and Amphisbaenians) Shows no Support for Key Traditional Morphological Relationships.
2020,
Pubmed
Camacho,
BLAST+: architecture and applications.
2009,
Pubmed
Chernomor,
Terrace Aware Data Structure for Phylogenomic Inference from Supermatrices.
2016,
Pubmed
Clouse,
Phylotranscriptomic analysis uncovers a wealth of tissue inhibitor of metalloproteinases variants in echinoderms.
2015,
Pubmed
,
Echinobase
Crotty,
Comparing Partitioned Models to Mixture Models: Do Information Criteria Apply?
2022,
Pubmed
Cunha,
Investigating Sources of Conflict in Deep Phylogenomics of Vetigastropod Snails.
2022,
Pubmed
Delsuc,
Phylogenomics and the reconstruction of the tree of life.
2005,
Pubmed
Dornburg,
Optimal Rates for Phylogenetic Inference and Experimental Design in the Era of Genome-Scale Data Sets.
2019,
Pubmed
Dunn,
Agalma: an automated phylogenomics workflow.
2013,
Pubmed
Faircloth,
Not all sequence tags are created equal: designing and validating sequence identification tags robust to indels.
2012,
Pubmed
Feuda,
Improved Modeling of Compositional Heterogeneity Supports Sponges as Sister to All Other Animals.
2017,
Pubmed
Francis,
Very few sites can reshape the inferred phylogenetic tree.
2020,
Pubmed
Gatesy,
Phylogenetic analysis at deep timescales: unreliable gene trees, bypassed hidden support, and the coalescence/concatalescence conundrum.
2014,
Pubmed
Gladyshev,
Massive horizontal gene transfer in bdelloid rotifers.
2008,
Pubmed
Grabherr,
Full-length transcriptome assembly from RNA-Seq data without a reference genome.
2011,
Pubmed
Guang,
Revising transcriptome assemblies with phylogenetic information.
2021,
Pubmed
Hoang,
UFBoot2: Improving the Ultrafast Bootstrap Approximation.
2018,
Pubmed
Janies,
EchinoDB, an application for comparative transcriptomics of deeply-sampled clades of echinoderms.
2016,
Pubmed
,
Echinobase
Jeffroy,
Phylogenomics: the beginning of incongruence?
2006,
Pubmed
Kalyaanamoorthy,
ModelFinder: fast model selection for accurate phylogenetic estimates.
2017,
Pubmed
Kapli,
Phylogenetic tree building in the genomic age.
2020,
Pubmed
Katoh,
MAFFT multiple sequence alignment software version 7: improvements in performance and usability.
2013,
Pubmed
King,
Embracing Uncertainty in Reconstructing Early Animal Evolution.
2017,
Pubmed
Kubatko,
Inconsistency of phylogenetic estimates from concatenated data under coalescence.
2007,
Pubmed
Kudtarkar,
Echinobase: an expanding resource for echinoderm genomic information.
2017,
Pubmed
Lartillot,
PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment.
2013,
Pubmed
Lartillot,
A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process.
2004,
Pubmed
Li,
Rooting the Animal Tree of Life.
2021,
Pubmed
Linchangco,
The phylogeny of extant starfish (Asteroidea: Echinodermata) including Xyloplax, based on comparative transcriptomics.
2017,
Pubmed
,
Echinobase
Lozano-Fernandez,
A Practical Guide to Design and Assess a Phylogenomic Study.
2022,
Pubmed
Mai,
TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees.
2018,
Pubmed
Miller,
Molecular phylogeny of extant Holothuroidea (Echinodermata).
2017,
Pubmed
,
Echinobase
Minh,
IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era.
2020,
Pubmed
Mirarab,
Evaluating Summary Methods for Multilocus Species Tree Estimation in the Presence of Incomplete Lineage Sorting.
2016,
Pubmed
Mongiardino Koch,
A phylogenomic resolution of the sea urchin tree of life.
2018,
Pubmed
,
Echinobase
Mongiardino Koch,
Confusion will be my epitaph: genome-scale discordance stifles phylogenetic resolution of Holothuroidea.
2023,
Pubmed
,
Echinobase
Mongiardino Koch,
Phylogenomic analyses of echinoid diversification prompt a re-evaluation of their fossil record.
2022,
Pubmed
,
Echinobase
Mongiardino Koch,
Phylogenomic Subsampling and the Search for Phylogenetically Reliable Loci.
2021,
Pubmed
Mongiardino Koch,
A Total-Evidence Dated Phylogeny of Echinoidea Combining Phylogenomic and Paleontological Data.
2021,
Pubmed
,
Echinobase
Morel,
ParGenes: a tool for massively parallel model selection and phylogenetic tree inference on thousands of genes.
2019,
Pubmed
O'Hara,
Phylogenomic resolution of the class Ophiuroidea unlocks a global microfossil record.
2014,
Pubmed
,
Echinobase
Ontano,
Taxonomic Sampling and Rare Genomic Changes Overcome Long-Branch Attraction in the Phylogenetic Placement of Pseudoscorpions.
2021,
Pubmed
Pandey,
Phylogenetic Analyses of Sites in Different Protein Structural Environments Result in Distinct Placements of the Metazoan Root.
2020,
Pubmed
Paradis,
ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R.
2019,
Pubmed
Philippe,
Mitigating Anticipated Effects of Systematic Errors Supports Sister-Group Relationship between Xenacoelomorpha and Ambulacraria.
2019,
Pubmed
,
Echinobase
Philippe,
Resolving difficult phylogenetic questions: why more sequences are not enough.
2011,
Pubmed
Quang,
Empirical profile mixture models for phylogenetic reconstruction.
2008,
Pubmed
Reddy,
Why Do Phylogenomic Data Sets Yield Conflicting Trees? Data Type Influences the Avian Tree of Life more than Taxon Sampling.
2017,
Pubmed
Redmond,
Evidence for sponges as sister to all other animals from partitioned phylogenomics with mixture models and recoding.
2021,
Pubmed
Richter,
Seed-based INTARNA prediction combined with GFP-reporter system identifies mRNA targets of the small RNA Yfr1.
2010,
Pubmed
Rota-Stabelli,
Serine codon-usage bias in deep phylogenomics: pancrustacean relationships as a case study.
2013,
Pubmed
Sayyari,
Fast Coalescent-Based Computation of Local Branch Support from Quartet Frequencies.
2016,
Pubmed
Schliep,
phangorn: phylogenetic analysis in R.
2011,
Pubmed
Schrempf,
Scalable Empirical Mixture Models That Account for Across-Site Compositional Heterogeneity.
2020,
Pubmed
Shen,
Contentious relationships in phylogenomic studies can be driven by a handful of genes.
2017,
Pubmed
Simon,
Reanalyzing the Palaeoptera problem - The origin of insect flight remains obscure.
2018,
Pubmed
Siu-Ting,
Inadvertent Paralog Inclusion Drives Artifactual Topologies and Timetree Estimates in Phylogenomics.
2019,
Pubmed
Smith,
Robust Analysis of Phylogenetic Tree Space.
2022,
Pubmed
Smith,
Phylogenetic Conflicts, Combinability, and Deep Phylogenomics in Plants.
2020,
Pubmed
Smith,
Phylogenomic Analysis of the Parrots of the World Distinguishes Artifactual from Biological Sources of Gene Tree Discordance.
2023,
Pubmed
Song,
Resolving conflict in eutherian mammal phylogeny using phylogenomics and the multispecies coalescent model.
2012,
Pubmed
Szánthó,
Compositionally Constrained Sites Drive Long-Branch Attraction.
2023,
Pubmed
Talavera,
Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments.
2007,
Pubmed
Townsend,
Phylogenetic signal and noise: predicting the power of a data set to resolve phylogeny.
2012,
Pubmed
Walker,
Concordance-Based Approaches for the Inference of Relationships and Molecular Rates with Phylogenomic Data Sets.
2022,
Pubmed
Wang,
Modeling Site Heterogeneity with Posterior Mean Site Frequency Profiles Accelerates Accurate Phylogenomic Estimation.
2018,
Pubmed
Whelan,
Who Let the CAT Out of the Bag? Accurately Dealing with Substitutional Heterogeneity in Phylogenomic Analyses.
2017,
Pubmed
Zhang,
ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees.
2018,
Pubmed