An amino acid substitution-selection model adjusts residue fitness to improve phylogenetic estimation

Is Over-parameterization a Problem for Profile Mixture Models?

Biochemical constraints on the admissible amino acids at specific sites in proteins lead to heterogeneity of the amino acid substitution process over sites in alignments. It is well known that phylogenetic models of protein sequence evolution that do not account for site heterogeneity are prone to long-branch attraction (LBA) artifacts. Profile mixture models were developed to model heterogeneity of preferred amino acids at sites via a finite distribution of site classes each with a distinct set of equilibrium amino acid frequencies. However, it is unknown whether the large number of parameters in such models associated with the many amino acid frequency vectors can adversely affect tree topology estimates because of over-parameterization. Here, we demonstrate theoretically that for long sequences, over-parameterization does not create problems for estimation with profile mixture models. Under mild conditions, tree, amino acid frequencies, and other model parameters converge to true values as sequence length increases, even when there are large numbers of components in the frequency profile distributions. Because large sample theory does not necessarily imply good behavior for shorter alignments we explore the performance of these models with short alignments simulated with tree topologies that are prone to LBA artifacts. We find that over-parameterization is not a problem for complex profile mixture models even when there are many amino acid frequency vectors. In fact, simple models with few site classes behave poorly. Interestingly, we also found that misspecification of the amino acid frequency vectors does not lead to increased LBA artifacts as long as the estimated cumulative distribution function of the amino acid frequencies at sites adequately approximates the true one. In contrast, misspecification of the amino acid exchangeability rates can severely negatively affect parameter estimation. Finally, we explore the effects of including in the profile mixture model an additional "F-class" representing the overall frequencies of amino acids in the data set. Surprisingly, the F-class does not help parameter estimation significantly and can decrease the probability of correct tree estimation, depending on the scenario, even though it tends to improve likelihood scores.

Parameter Identifiability for a Profile Mixture Model of Protein Evolution

A profile mixture (PM) model is a model of protein evolution, describing sequence data in which sites are assumed to follow many related substitution processes on a single evolutionary tree. The processes depend, in part, on different amino acid distributions, or profiles, varying over sites in aligned sequences. A fundamental question for any stochastic model, which must be answered positively to justify model-based inference, is whether the parameters are identifiable from the probability distribution they determine. Here, using algebraic methods, we show that a PM model has identifiable parameters under circumstances in which it is likely to be used for empirical analyses. In particular, for a tree relating 9 or more taxa, both the tree topology and all numerical parameters are generically identifiable when the number of profiles is less than 74.

Evidence for sponges as sister to all other animals from partitioned phylogenomics with mixture models and recoding

Resolving the relationships between the major lineages in the animal tree of life is necessary to understand the origin and evolution of key animal traits. Sponges, characterized by their simple body plan, were traditionally considered the sister group of all other animal lineages, implying a gradual increase in animal complexity from unicellularity to complex multicellularity. However, the availability of genomic data has sparked tremendous controversy as some phylogenomic studies support comb jellies taking this position, requiring secondary loss or independent origins of complex traits. Here we show that incorporating site-heterogeneous mixture models and recoding into partitioned phylogenomics alleviates systematic errors that hamper commonly-applied phylogenetic models. Testing on real datasets, we show a great improvement in model-fit that attenuates branching artefacts induced by systematic error. We reanalyse key datasets and show that partitioned phylogenomics does not support comb jellies as sister to other animals at either the supermatrix or partition-specific level.

The Relative Importance of Modeling Site Pattern Heterogeneity Versus Partition-Wise Heterotachy in Phylogenomic Inference

Large taxa-rich genome-scale data sets are often necessary for resolving ancient phylogenetic relationships. But accurate phylogenetic inference requires that they are analyzed with realistic models that account for the heterogeneity in substitution patterns amongst the sites, genes and lineages. Two kinds of adjustments are frequently used: models that account for heterogeneity in amino acid frequencies at sites in proteins, and partitioned models that accommodate the heterogeneity in rates (branch lengths) among different proteins in different lineages (protein-wise heterotachy). Although partitioned and site-heterogeneous models are both widely used in isolation, their relative importance to the inference of correct phylogenies has not been carefully evaluated. We conducted several empirical analyses and a large set of simulations to compare the relative performances of partitioned models, site-heterogeneous models, and combined partitioned site heterogeneous models. In general, site-homogeneous models (partitioned or not) performed worse than site heterogeneous, except in simulations with extreme protein-wise heterotachy. Furthermore, simulations using empirically-derived realistic parameter settings showed a marked long-branch attraction (LBA) problem for analyses employing protein-wise partitioning even when the generating model included partitioning. This LBA problem results from a small sample bias compounded over many single protein alignments. In some cases, this problem was ameliorated by clustering similarly-evolving proteins together into larger partitions using the PartitionFinder method. Similar results were obtained under simulations with larger numbers of taxa or heterogeneity in simulating topologies over genes. For an empirical Microsporidia test data set, all but one tested site-heterogeneous models (with or without partitioning) obtain the correct Microsporidia+Fungi grouping, whereas site-homogenous models (with or without partitioning) did not. The single exception was the fully partitioned site-heterogeneous analysis that succumbed to the compounded small sample LBA bias. In general unless protein-wise heterotachy effects are extreme, it is more important to model site-heterogeneity than protein-wise heterotachy in phylogenomic analyses. Complete protein-wise partitioning should be avoided as it can lead to a serious LBA bias. In cases of extreme protein-wise heterotachy, approaches that cluster similarly-evolving proteins together and coupled with site-heterogeneous models work well for phylogenetic estimation.

Accelerated Estimation of Frequency Classes in Site-Heterogeneous Profile Mixture Models

As a consequence of structural and functional constraints, proteins tend to have site-specific preferences for particular amino acids. Failing to adjust for heterogeneity of frequencies over sites can lead to artifacts in phylogenetic estimation. Site-heterogeneous mixture-models have been developed to address this problem. However, due to prohibitive computational times, maximum likelihood implementations utilize fixed component frequency vectors inferred from sequences in a database that are external to the alignment under analysis. Here, we propose a composite likelihood approach to estimation of component frequencies for a mixture model that directly uses the data from the alignment of interest. In the common case that the number of taxa under study is not large, several adjustments to the default composite likelihood are shown to be necessary. In simulations, the approach is shown to provide large improvements over hierarchical clustering. For empirical data, substantial improvements in likelihoods are found over mixtures using fixed components.

A Surrogate Function for One-Dimensional Phylogenetic Likelihoods

Phylogenetics has seen a steady increase in data set size and substitution model complexity, which require increasing amounts of computational power to compute likelihoods. This motivates strategies to approximate the likelihood functions for branch length optimization and Bayesian sampling. In this article, we develop an approximation to the 1D likelihood function as parametrized by a single branch length. Our method uses a four-parameter surrogate function abstracted from the simplest phylogenetic likelihood function, the binary symmetric model. We show that it offers a surrogate that can be fit over a variety of branch lengths, that it is applicable to a wide variety of models and trees, and that it can be used effectively as a proposal mechanism for Bayesian sampling. The method is implemented as a stand-alone open-source C library for calling from phylogenetics algorithms; it has proven essential for good performance of our online phylogenetic algorithm sts.

Phylotranscriptomics suggests the jawed vertebrate ancestor could generate diverse helper and regulatory T cell subsets

BACKGROUND

The cartilaginous fishes diverged from other jawed vertebrates ~ 450 million years ago (mya). Despite this key evolutionary position, the only high-quality cartilaginous fish genome available is for the elephant shark (Callorhinchus milii), a chimaera whose ancestors split from the elasmobranch lineage ~ 420 mya. Initial analysis of this resource led to proposals that key components of the cartilaginous fish adaptive immune system, most notably their array of T cell subsets, was primitive compared to mammals. This proposal is at odds with the robust, antigen-specific antibody responses reported in elasmobranchs following immunization. To explore this discrepancy, we generated a multi-tissue transcriptome for small-spotted catshark (Scyliorhinus canicula), a tractable elasmobranch model for functional studies. We searched this, and other newly available sequence datasets, for CD4+ T cell subset-defining genes, aiming to confirm the presence or absence of each subset in cartilaginous fishes.

RESULTS

We generated a new transcriptome based on a normalised, multi-tissue RNA pool, aiming to maximise representation of tissue-specific and lowly expressed genes. We utilized multiple transcriptomic datasets and assembly variants in phylogenetic reconstructions to unambiguously identify several T cell subset-specific molecules in cartilaginous fishes for the first time, including interleukins, interleukin receptors, and key transcription factors. Our results reveal the inability of standard phylogenetic reconstruction approaches to capture the site-specific evolutionary processes of fast-evolving immune genes but show that site-heterogeneous mixture models can adequately do so.

CONCLUSIONS

Our analyses reveal that cartilaginous fishes are capable of producing a range of CD4+ T cell subsets comparable to that of mammals. Further, that the key molecules required for the differentiation and functioning of these subsets existed in the jawed vertebrate ancestor. Additionally, we highlight the importance of considering phylogenetic diversity and, where possible, utilizing multiple datasets for individual species, to accurately infer gene presence or absence at higher taxonomic levels.

Modeling Site Heterogeneity with Posterior Mean Site Frequency Profiles Accelerates Accurate Phylogenomic Estimation

Proteins have distinct structural and functional constraints at different sites that lead to site-specific preferences for particular amino acid residues as the sequences evolve. Heterogeneity in the amino acid substitution process between sites is not modeled by commonly used empirical amino acid exchange matrices. Such model misspecification can lead to artefacts in phylogenetic estimation such as long-branch attraction. Although sophisticated site-heterogeneous mixture models have been developed to address this problem in both Bayesian and maximum likelihood (ML) frameworks, their formidable computational time and memory usage severely limits their use in large phylogenomic analyses. Here we propose a posterior mean site frequency (PMSF) method as a rapid and efficient approximation to full empirical profile mixture models for ML analysis. The PMSF approach assigns a conditional mean amino acid frequency profile to each site calculated based on a mixture model fitted to the data using a preliminary guide tree. These PMSF profiles can then be used for in-depth tree-searching in place of the full mixture model. Compared with widely used empirical mixture models with $k$ classes, our implementation of PMSF in IQ-TREE (http://www.iqtree.org) speeds up the computation by approximately $k$/1.5-fold and requires a small fraction of the RAM. Furthermore, this speedup allows, for the first time, full nonparametric bootstrap analyses to be conducted under complex site-heterogeneous models on large concatenated data matrices. Our simulations and empirical data analyses demonstrate that PMSF can effectively ameliorate long-branch attraction artefacts. In some empirical and simulation settings PMSF provided more accurate estimates of phylogenies than the mixture models from which they derive.

Between a Pod and a Hard Test: The Deep Evolution of Amoebae

Amoebozoa is the eukaryotic supergroup sister to Obazoa, the lineage that contains the animals and Fungi, as well as their protistan relatives, and the breviate and apusomonad flagellates. Amoebozoa is extraordinarily diverse, encompassing important model organisms and significant pathogens. Although amoebozoans are integral to global nutrient cycles and present in nearly all environments, they remain vastly understudied. We present a robust phylogeny of Amoebozoa based on broad representative set of taxa in a phylogenomic framework (325 genes). By sampling 61 taxa using culture-based and single-cell transcriptomics, our analyses show two major clades of Amoebozoa, Discosea, and Tevosa. This phylogeny refutes previous studies in major respects. Our results support the hypothesis that the last common ancestor of Amoebozoa was sexual and flagellated, it also may have had the ability to disperse propagules from a sporocarp-type fruiting body. Overall, the main macroevolutionary patterns in Amoebozoa appear to result from the parallel losses of homologous characters of a multiphase life cycle that included flagella, sex, and sporocarps rather than independent acquisition of convergent features.

SubVis: an interactive R package for exploring the effects of multiple substitution matrices on pairwise sequence alignment

Understanding how proteins mutate is critical to solving a host of biological problems. Mutations occur when an amino acid is substituted for another in a protein sequence. The set of likelihoods for amino acid substitutions is stored in a matrix and input to alignment algorithms. The quality of the resulting alignment is used to assess the similarity of two or more sequences and can vary according to assumptions modeled by the substitution matrix. Substitution strategies with minor parameter variations are often grouped together in families. For example, the BLOSUM and PAM matrix families are commonly used because they provide a standard, predefined way of modeling substitutions. However, researchers often do not know if a given matrix family or any individual matrix within a family is the most suitable. Furthermore, predefined matrix families may inaccurately reflect a particular hypothesis that a researcher wishes to model or otherwise result in unsatisfactory alignments. In these cases, the ability to compare the effects of one or more custom matrices may be needed. This laborious process is often performed manually because the ability to simultaneously load multiple matrices and then compare their effects on alignments is not readily available in current software tools. This paper presents SubVis, an interactive R package for loading and applying multiple substitution matrices to pairwise alignments. Users can simultaneously explore alignments resulting from multiple predefined and custom substitution matrices. SubVis utilizes several of the alignment functions found in R, a common language among protein scientists. Functions are tied together with the Shiny platform which allows the modification of input parameters. Information regarding alignment quality and individual amino acid substitutions is displayed with the JavaScript language which provides interactive visualizations for revealing both high-level and low-level alignment information.

Nuclear genetic codes with a different meaning of the UAG and the UAA codon

BACKGROUND

Departures from the standard genetic code in eukaryotic nuclear genomes are known for only a handful of lineages and only a few genetic code variants seem to exist outside the ciliates, the most creative group in this regard. Most frequent code modifications entail reassignment of the UAG and UAA codons, with evidence for at least 13 independent cases of a coordinated change in the meaning of both codons. However, no change affecting each of the two codons separately has been documented, suggesting the existence of underlying evolutionary or mechanistic constraints.

RESULTS

Here, we present the discovery of two new variants of the nuclear genetic code, in which UAG is translated as an amino acid while UAA is kept as a termination codon (along with UGA). The first variant occurs in an organism noticed in a (meta)transcriptome from the heteropteran Lygus hesperus and demonstrated to be a novel insect-dwelling member of Rhizaria (specifically Sainouroidea). This first documented case of a rhizarian with a non-canonical genetic code employs UAG to encode leucine and represents an unprecedented change among nuclear codon reassignments. The second code variant was found in the recently described anaerobic flagellate Iotanema spirale (Metamonada: Fornicata). Analyses of transcriptomic data revealed that I. spirale uses UAG to encode glutamine, similarly to the most common variant of a non-canonical code known from several unrelated eukaryotic groups, including hexamitin diplomonads (also a lineage of fornicates). However, in these organisms, UAA also encodes glutamine, whereas it is the primary termination codon in I. spirale. Along with phylogenetic evidence for distant relationship of I. spirale and hexamitins, this indicates two independent genetic code changes in fornicates.

CONCLUSIONS

Our study documents, for the first time, that evolutionary changes of the meaning of UAG and UAA codons in nuclear genomes can be decoupled and that the interpretation of the two codons by the cytoplasmic translation apparatus is mechanistically separable. The latter conclusion has interesting implications for possibilities of genetic code engineering in eukaryotes. We also present a newly developed generally applicable phylogeny-informed method for inferring the meaning of reassigned codons.

Expansion of the molecular and morphological diversity of Acanthamoebidae (Centramoebida, Amoebozoa) and identification of a novel life cycle type within the group

BACKGROUND

Acanthamoebidae is a "family" level amoebozoan group composed of the genera Acanthamoeba, Protacanthamoeba, and very recently Luapeleamoeba. This clade of amoebozoans has received considerable attention from the broader scientific community as Acanthamoeba spp. represent both model organisms and human pathogens. While the classical composition of the group (Acanthamoeba + Protacanthamoeba) has been well accepted due to the morphological and ultrastructural similarities of its members, the Acanthamoebidae has never been highly statistically supported in single gene phylogenetic reconstructions of Amoebozoa either by maximum likelihood (ML) or Bayesian analyses.

RESULTS

Here we show using a phylogenomic approach that the Acanthamoebidae is a fully supported monophyletic group within Amoebozoa with both ML and Bayesian analyses. We also expand the known range of morphological and life cycle diversity found in the Acanthamoebidae by demonstrating that the amoebozoans "Protostelium" arachisporum, Dracoamoeba jormungandri n. g. n. sp., and Vacuolamoeba acanthoformis n.g. n.sp., belong within the group. We also found that "Protostelium" pyriformis is clearly a species of Acanthamoeba making it the first reported sporocarpic member of the genus, that is, an amoeba that individually forms a walled, dormant propagule elevated by a non-cellular stalk. Our phylogenetic analyses recover a fully supported Acanthamoebidae composed of five genera. Two of these genera (Acanthamoeba and Luapeleameoba) have members that are sporocarpic.

CONCLUSIONS

Our results provide high statistical support for an Acanthamoebidae that is composed of five distinct genera. This study increases the known morphological diversity of this group and shows that species of Acanthamoeba can include spore-bearing stages. This further illustrates the widespread nature of spore-bearing stages across the tree of Amoebozoa.

REVIEWERS

This article was reviewed by Drs. Eugene Koonin, Purificacion Lopez-Garcia and Sandra Baldauf. Sandra Baldauf was nominated by Purificacion Lopez-Garcia, an Editorial Board member.

Heme pathway evolution in kinetoplastid protists

Background

Kinetoplastea is a diverse protist lineage composed of several of the most successful parasites on Earth, organisms whose metabolisms have coevolved with those of the organisms they infect. Parasitic kinetoplastids have emerged from free-living, non-pathogenic ancestors on multiple occasions during the evolutionary history of the group. Interestingly, in both parasitic and free-living kinetoplastids, the heme pathway—a core metabolic pathway in a wide range of organisms—is incomplete or entirely absent. Indeed, Kinetoplastea investigated thus far seem to bypass the need for heme biosynthesis by acquiring heme or intermediate metabolites directly from their environment.

Results

Here we report the existence of a near-complete heme biosynthetic pathway in Perkinsela spp., kinetoplastids that live as obligate endosymbionts inside amoebozoans belonging to the genus Paramoeba/Neoparamoeba. We also use phylogenetic analysis to infer the evolution of the heme pathway in Kinetoplastea.

Conclusion

We show that Perkinsela spp. is a deep-branching kinetoplastid lineage, and that lateral gene transfer has played a role in the evolution of heme biosynthesis in Perkinsela spp. and other Kinetoplastea. We also discuss the significance of the presence of seven of eight heme pathway genes in the Perkinsela genome as it relates to its endosymbiotic relationship with Paramoeba.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0664-6) contains supplementary material, which is available to authorized users.

First multigene analysis of Archamoebae (Amoebozoa: Conosa) robustly reveals its phylogeny and shows that Entamoebidae represents a deep lineage of the group

Archamoebae is an understudied group of anaerobic free-living or endobiotic protists that constitutes the major anaerobic lineage of the supergroup Amoebozoa. Hitherto, the phylogeny of Archamoebae was based solely on SSU rRNA and actin genes, which did not resolve relationships among the main lineages of the group. Because of this uncertainty, several different scenarios had been proposed for the phylogeny of the Archamoebae. In this study, we present the first multigene phylogenetic analysis that includes members of Pelomyxidae, and Rhizomastixidae. The analysis clearly shows that Mastigamoebidae, Pelomyxidae and Rhizomastixidae form a clade of mostly free-living, amoeboid flagellates, here called Pelobiontida. The predominantly endobiotic and aflagellated Entamoebidae represents a separate, deep-branching lineage, Entamoebida. Therefore, two unique evolutionary events, horizontal transfer of the nitrogen fixation system from bacteria and transfer of the sulfate activation pathway to mitochondrial derivatives, predate the radiation of recent lineages of Archamoebae. The endobiotic lifestyle has arisen at least three times independently during the evolution of the group. We also present new ultrastructural data that clarifies the primary divergence among the family Mastigamoebidae which had previously been inferred from phylogenetic analyses based on SSU rDNA.

Untangling the early diversification of eukaryotes: a phylogenomic study of the evolutionary origins of Centrohelida, Haptophyta and Cryptista

Assembling the global eukaryotic tree of life has long been a major effort of Biology. In recent years, pushed by the new availability of genome-scale data for microbial eukaryotes, it has become possible to revisit many evolutionary enigmas. However, some of the most ancient nodes, which are essential for inferring a stable tree, have remained highly controversial. Among other reasons, the lack of adequate genomic datasets for key taxa has prevented the robust reconstruction of early diversification events. In this context, the centrohelid heliozoans are particularly relevant for reconstructing the tree of eukaryotes because they represent one of the last substantial groups that was missing large and diverse genomic data. Here, we filled this gap by sequencing high-quality transcriptomes for four centrohelid lineages, each corresponding to a different family. Combining these new data with a broad eukaryotic sampling, we produced a gene-rich taxon-rich phylogenomic dataset that enabled us to refine the structure of the tree. Specifically, we show that (i) centrohelids relate to haptophytes, confirming Haptista; (ii) Haptista relates to SAR; (iii) Cryptista share strong affinity with Archaeplastida; and (iv) Haptista + SAR is sister to Cryptista + Archaeplastida. The implications of this topology are discussed in the broader context of plastid evolution.

Probabilistic models of eukaryotic evolution: time for integration

In spite of substantial work and recent progress, a global and fully resolved picture of the macroevolutionary history of eukaryotes is still under construction. This concerns not only the phylogenetic relations among major groups, but also the general characteristics of the underlying macroevolutionary processes, including the patterns of gene family evolution associated with endosymbioses, as well as their impact on the sequence evolutionary process. All these questions raise formidable methodological challenges, calling for a more powerful statistical paradigm. In this direction, model-based probabilistic approaches have played an increasingly important role. In particular, improved models of sequence evolution accounting for heterogeneities across sites and across lineages have led to significant, although insufficient, improvement in phylogenetic accuracy. More recently, one main trend has been to move away from simple parametric models and stepwise approaches, towards integrative models explicitly considering the intricate interplay between multiple levels of macroevolutionary processes. Such integrative models are in their infancy, and their application to the phylogeny of eukaryotes still requires substantial improvement of the underlying models, as well as additional computational developments.

Parameterized BLOSUM Matrices for Protein Alignment

Protein alignment is a basic step for many molecular biology researches. The BLOSUM matrices, especially BLOSUM62, are the de facto standard matrices for protein alignments. However, after widely utilization of the matrices for 15 years, programming errors were surprisingly found in the initial version of source codes for their generation. And amazingly, after bug correction, the "intended" BLOSUM62 matrix performs consistently worse than the "miscalculated" one. In this paper, we find linear relationships among the eigenvalues of the matrices and propose an algorithm to find optimal unified eigenvectors. With them, we can parameterize matrix BLOSUMx for any given variable x that could change continuously. We compare the effectiveness of our parameterized isentropic matrix with BLOSUM62. Furthermore, an iterative alignment and matrix selection process is proposed to adaptively find the best parameter and globally align two sequences. Experiments are conducted on aligning 13,667 families of Pfam database and on clustering MHC II protein sequences, whose improved accuracy demonstrates the effectiveness of our proposed method.