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Somatolactin alpha (SLα) is a fish-specific hormone involved in body color regulation. The growth hormone (GH) is another hormone that is expressed in all vertebrates and promotes growth. These peptide hormones act by binding to receptors (SLα receptor (SLR) and GH receptor (GHR)); however, the relationships between these ligands and their receptors vary among species. Here, we first performed phylogenetic tree reconstruction by collecting the amino-acid sequences classified as SLR, GHR, or GHR-like from bony fish. Second, we impaired SLR or GHR functions in medaka (Oryzias sakaizumii) using CRISPR/Cas9. Lastly, we analyzed SLR and GHR mutants for phenotypes to deduce their functions. Phylogenetic tree reconstruction was performed using a total of 222 amino-acid sequences from 136 species, which revealed that many GHRa and GHRb are vaguely termed as GHR or GHR-like, while showing no orthologous/paralogous relationships. SLR and GHR mutants were successfully established for phenotyping. SLR mutants exhibited premature lethality after hatching, indicating an essential role for SLR in normal growth. GHR mutations did not affect viability, body length, or body color. These results provide no evidence that either SLR or GHR functions as a receptor for SLα; rather, phylogenetically and functionally, they seem to be receptors for GH, although their (subfunctionalized) roles warrant further investigation.

This book presents a history of microbial evolutionary biology from the 19th century to the present. It follows the research of molecular evolutionists who explore the origins of the genetic system and the primary life forms: three domains and multiple kingdoms, created by mechanisms very unlike those considered by Darwin and his followers.

Aneuploidy, chromosomal instability, somatic copy-number alterations, and whole-genome doubling (WGD) play key roles in cancer evolution and provide information for the complex task of phylogenetic inference. We present MEDICC2, a method for inferring evolutionary trees and WGD using haplotype-specific somatic copy-number alterations from single-cell or bulk data. MEDICC2 eschews simplifications such as the infinite sites assumption, allowing multiple mutations and parallel evolution, and does not treat adjacent loci as independent, allowing overlapping copy-number events. Using simulations and multiple data types from 2780 tumors, we use MEDICC2 to demonstrate accurate inference of phylogenies, clonal and subclonal WGD, and ancestral copy-number states.

The BAHD acyltransferase family is a class of proteins in plants that can acylate a variety of primary and specialized secondary metabolites. The typically acylated products have greatly improved stability, lipid solubility, and bioavailability and thus show significant differences in their physicochemical properties and pharmacological activities. Here, we review the protein structure, catalytic mechanism, and phylogenetic reconstruction of plant BAHD acyltransferases to describe their family characteristics, acylation reactions, and the processes of potential functional differentiation. Moreover, the potential applications of the BAHD family in human activities are discussed from the perspectives of improving the quality of economic plants, enhancing the efficacy of medicinal plants, improving plant biomass for use in biofuel, and promoting stress resistance of land plants. This review provides a reference for the research and production of plant BAHD acyltransferases.

Statistical criteria have long been the standard for selecting the best model for phylogenetic reconstruction and downstream statistical inference. Although model selection is regarded as a fundamental step in phylogenetics, existing methods for this task consume computational resources for long processing time, they are not always feasible, and sometimes depend on preliminary assumptions which do not hold for sequence data. Moreover, although these methods are dedicated to revealing the processes that underlie the sequence data, they do not always produce the most accurate trees. Notably, phylogeny reconstruction consists of two related tasks, topology reconstruction and branch-length estimation. It was previously shown that in many cases the most complex model, GTR+I+G, leads to topologies that are as accurate as using existing model selection criteria, but overestimates branch lengths. Here, we present ModelTeller, a computational methodology for phylogenetic model selection, devised within the machine-learning framework, optimized to predict the most accurate nucleotide substitution model for branch-length estimation. We demonstrate that ModelTeller leads to more accurate branch-length inference than current model selection criteria on data sets simulated under realistic processes. ModelTeller relies on a readily implemented machine-learning model and thus the prediction according to features extracted from the sequence data results in a substantial decrease in running time compared with existing strategies. By harnessing the machine-learning framework, we distinguish between features that mostly contribute to branch-length optimization, concerning the extent of sequence divergence, and features that are related to estimates of the model parameters that are important for the selection made by current criteria.

BACKGROUND: Phylogenetic and population genetic studies often deal with multiple sequence alignments that require manipulation or processing steps such as sequence concatenation, sequence renaming, sequence translation or consensus sequence generation. In recent years phylogenetic data sets have expanded from single genes to genome wide markers comprising hundreds to thousands of loci. Processing of these large phylogenomic data sets is impracticable without using automated process pipelines. Currently no stand-alone or pipeline compatible program exists that offers a broad range of manipulation and processing steps for multiple sequence alignments in a single process run. RESULTS: Here we present FASconCAT-G, a system independent editor, which offers various processing options for multiple sequence alignments. The software provides a wide range of possibilities to edit and concatenate multiple nucleotide, amino acid, and structure sequence alignment files for phylogenetic and population genetic purposes. The main options include sequence renaming, file format conversion, sequence translation between nucleotide and amino acid states, consensus generation of specific sequence blocks, sequence concatenation, model selection of amino acid replacement with ProtTest, two types of RY coding as well as site exclusions and extraction of parsimony informative sites. Convieniently, most options can be invoked in combination and performed during a single process run. Additionally, FASconCAT-G prints useful information regarding alignment characteristics and editing processes such as base compositions of single in- and outfiles, sequence areas in a concatenated supermatrix, as well as paired stem and loop regions in secondary structure sequence strings. CONCLUSIONS: FASconCAT-G is a command-line driven Perl program that delivers computationally fast and user-friendly processing of multiple sequence alignments for phylogenetic and population genetic applications and is well suited for incorporation into analysis pipelines.

Phylogenetic inference aims at reconstructing the tree describing the evolution of a set of sequences descending from a common ancestor. The high computational cost of state-of-the-art maximum likelihood and Bayesian inference methods limits their usability under realistic evolutionary models. Harnessing recent advances in likelihood-free inference and geometric deep learning, we introduce Phyloformer, a fast and accurate method for evolutionary distance estimation and phylogenetic reconstruction. Sampling many trees and sequences under an evolutionary model, we train the network to learn a function that enables predicting a tree from a multiple sequence alignment. On simulated data, we compare Phyloformer to FastME—a distance method—and two maximum likelihood methods: FastTree and IQTree. Under a commonly used model of protein sequence evolution and exploiting graphics processing unit (GPU) acceleration, Phyloformer outpaces all other approaches and exceeds their accuracy in the Kuhner–Felsenstein metric that accounts for both the topology and branch lengths. In terms of topological accuracy alone, Phyloformer outperforms FastME, but falls behind maximum likelihood approaches, especially as the number of sequences increases. When a model of sequence evolution that includes dependencies between sites is used, Phyloformer outperforms all other methods across all metrics on alignments with fewer than 80 sequences. On 3,801 empirical gene alignments from five different datasets, Phyloformer matches the topological accuracy of the two maximum likelihood implementations. Our results pave the way for the adoption of sophisticated realistic models for phylogenetic inference.

The 16S ribosomal RNA gene is the most widely used marker gene in microbial ecology. Counts of 16S sequence variants, often in PCR amplicons, are used to estimate proportions of bacterial and archaeal taxa in microbial communities. Because different organisms contain different 16S gene copy numbers (GCNs), sequence variant counts are biased towards clades with greater GCNs. Several tools have recently been developed for predicting GCNs using phylogenetic methods and based on sequenced genomes, in order to correct for these biases. However, the accuracy of those predictions has not been independently assessed. Here, we systematically evaluate the predictability of 16S GCNs across bacterial and archaeal clades, based on ∼ 6,800 public sequenced genomes and using several phylogenetic methods. Further, we assess the accuracy of GCNs predicted by three recently published tools (PICRUSt, CopyRighter, and PAPRICA) over a wide range of taxa and for 635 microbial communities from varied environments. We find that regardless of the phylogenetic method tested, 16S GCNs could only be accurately predicted for a limited fraction of taxa, namely taxa with closely to moderately related representatives (≲15% divergence in the 16S rRNA gene). Consistent with this observation, we find that all considered tools exhibit low predictive accuracy when evaluated against completely sequenced genomes, in some cases explaining less than 10% of the variance. Substantial disagreement was also observed between tools ( R 2 <0.5) for the majority of tested microbial communities. The nearest sequenced taxon index (NSTI) of microbial communities, i.e., the average distance to a sequenced genome, was a strong predictor for the agreement between GCN prediction tools on non-animal-associated samples, but only a moderate predictor for animal-associated samples. We recommend against correcting for 16S GCNs in microbiome surveys by default, unless OTUs are sufficiently closely related to sequenced genomes or unless a need for true OTU proportions warrants the additional noise introduced, so that community profiles remain interpretable and comparable between studies.

Reconstruction of an ancestral genome for a set of plant species has been a challenging task because of complex histories that may include whole-genome duplications, segmental duplications, independent gene duplications or losses, diploidization and rearrangement events. Here, we describe the reconstruction a hypothetical ancestral genome for the papilionoid legumes (the largest subfamily within the third largest family in flowering plants), and evaluate the results relative to phylogenetic and chromosomal count data for this group of legumes, spanning 294 diverse papilionoid genera. To reconstruct the ancestral genomes for nine legume species with sequenced genomes, we used a maximum likelihood approach combined with a novel method for identifying informative markers for this purpose. Analyzing genomes from four species within the Phaseoleae, two in Dalbergieae, two in the ‘inverted repeat loss’ clade, and one in the Robinieae, we infer a common ancestral genome with nine chromosomes. The reconstructed genome structural histories are consistent with chromosomal and phylogenetic histories, but we also infer that a common ancestor with nine chromosomes was probably intermediate to an earlier state of 14 chromosomes following a whole-genome duplication that pre-dated the radiation of the papilionoid legumes, evidence for which is found in early-diverging papilionoid lineages.

Despite impressive technical and theoretical developments, reconstruction of phylogenetic trees for enormous quantities of molecular data is still a challenging task. A key tool in analyses of large data sets has been the construction of separate trees for subsets (e.g., quartets) of sequences, and subsequent combination of these subtrees into a single tree for the full set (i.e., supertree analysis). Unfortunately, even amalgamating quartets into a supertree remains a computationally daunting task. Assigning weights to quartets to indicate importance or reliability was proposed more than a decade ago, but handling weighted quartets is even more challenging and has scarcely been attempted in the past. In this work, we focus on weighted quartet-based approaches. We propose a scheme to assign weights to quartets coming from weighted trees and devise a tree similarity measure for weighted trees based on weighted quartets. We also extend the quartet MaxCut (QMC algorithm) to handle weighted quartets. We evaluate these tools on simulated and real data. Our simulated data analysis highlights the additional information that is conveyed when using the new weighted tree similarity measure, and shows that extending QMC to a weighted setting improves the quality of tree reconstruction. Our analyses of a cyanobacterial data set with weighted QMC reinforce previous results achieved with other tools.