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\"What can we learn from the genes of our closest evolutionary relatives? Neanderthal Man tells the story of geneticist Svante Pääbo's mission to answer that question, beginning with the study of DNA in Egyptian mummies in the early 1980s and culminating in his sequencing of the Neanderthal genome in 2009. From Pääbo, we learn how Neanderthal genes offer a unique window into the lives of our hominin relatives and may hold the key to unlocking the mystery of why humans survived while Neanderthals went extinct. Drawing on genetic and fossil clues, Pääbo explores what is known about the origin of modern humans and their relationship to the Neanderthals and describes the fierce debate surrounding the nature of the two species' interactions. A riveting story about a visionary researcher and the nature of scientific inquiry, Neanderthal Man offers rich insight into the fundamental question of who we are\"-- Provided by publisher.

IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

DNA methylation of transcription units (gene bodies) occurs in the genomes of many animal and plant species. Phylogenetic persistence of gene body methylation implies biological significance; yet, the functional roles of gene body methylation remain elusive. In this study, we analyzed methylation levels of orthologs from four distantly related invertebrate species, including the honeybee, silkworm, sea squirt, and sea anemone. We demonstrate that in all four species, gene bodies distinctively cluster to two groups, which correspond to high and low methylation levels. This pattern resembles that of sequence composition arising from the mutagenetic effect of DNA methylation. In spite of this effect, our results show that protein sequences of genes targeted by high levels of methylation are conserved relative to genes lacking methylation. Our investigation identified many genes that either gained or lost methylation during the course of invertebrate evolution. Most of these genes appear to have lost methylation in the insect lineages we investigated, particularly in the honeybee. We found that genes that are methylated in all four invertebrate taxa are enriched for housekeeping functions related to transcription and translation, whereas the loss of DNA methylation occurred in genes whose functions include cellular signaling and reproductive processes. Overall, our study helps to illuminate the functional significance of gene body methylation and its impacts on genome evolution in diverse invertebrate taxa.

Efficient algorithms and programs for the analysis of the ever-growing amount of biological sequence data are strongly needed in the genomics era. The pace at which new data and methodologies are generated calls for the use of pre-existing, optimized—yet extensible—code, typically distributed as libraries or packages. This motivated the Bio++ project, aiming at developing a set of C++ libraries for sequence analysis, phylogenetics, population genetics, and molecular evolution. The main attractiveness of Bio++ is the extensibility and reusability of its components through its object-oriented design, without compromising the computer-efficiency of the underlying methods. We present here the second major release of the libraries, which provides an extended set of classes and methods. These extensions notably provide built-in access to sequence databases and new data structures for handling and manipulating sequences from the omics era, such as multiple genome alignments and sequencing reads libraries. More complex models of sequence evolution, such as mixture models and generic n-tuples alphabets, are also included.

Gene duplication plays key roles in organismal evolution. Duplicate genes, if they survive, tend to diverge in regulatory and coding regions. Divergences in coding regions, especially those that can change the function of the gene, can be caused by amino acid-altering substitutions and/or alterations in exon–intron structure. Much has been learned about the mode, tempo, and consequences of nucleotide substitutions, yet relatively little is known about structural divergences. In this study, by analyzing 612 pairs of sibling paralogs from seven representative gene families and 300 pairs of one-to-one orthologs from different species, we investigated the occurrence and relative importance of structural divergences during the evolution of duplicate and nonduplicate genes. We found that structural divergences have been very prevalent in duplicate genes and, in many cases, have led to the generation of functionally distinct paralogs. Comparisons of the genomic sequences of these genes further indicated that the differences in exon–intron structure were actually accomplished by three main types of mechanisms (exon/intron gain/loss, exonization/pseudoexonization, and insertion/deletion), each of which contributed differently to structural divergence. Like nucleotide substitutions, insertion/deletion and exonization/pseudoexonization occurred more or less randomly, with the number of observable mutational events per gene pair being largely proportional to evolutionary time. Notably, however, compared with paralogs with similar evolutionary times, orthologs have accumulated significantly fewer structural changes, whereas the amounts of amino acid replacements accumulated did not show clear differences. This finding suggests that structural divergences have played a more important role during the evolution of duplicate than nonduplicate genes.

Abstract The transcription factor and cell cycle regulator p53 is marked for degradation by the ubiquitin ligase MDM2. The interaction between these 2 proteins is mediated by a conserved binding motif in the disordered p53 transactivation domain (p53TAD) and the folded SWIB domain in MDM2. The conserved motif in p53TAD from zebrafish displays a 20-fold weaker interaction with MDM2, compared to the interaction in human and chicken. To investigate this apparent difference, we tracked the molecular evolution of the p53TAD/MDM2 interaction among ray-finned fishes (Actinopterygii), the largest vertebrate clade. Intriguingly, phylogenetic analyses, ancestral sequence reconstructions, and binding experiments showed that different loss-of-affinity changes in the canonical binding motif within p53TAD have occurred repeatedly and convergently in different fish lineages, resulting in relatively low extant affinities (KD = 0.5 to 5 μM). However, for 11 different fish p53TAD/MDM2 interactions, nonconserved regions flanking the canonical motif increased the affinity 4- to 73-fold to be on par with the human interaction. Our findings suggest that compensating changes at conserved and nonconserved positions within the motif, as well as in flanking regions of low conservation, underlie a stabilizing selection of “functional affinity” in the p53TAD/MDM2 interaction. Such interplay complicates bioinformatic prediction of binding and calls for experimental validation. Motif-mediated protein–protein interactions involving short binding motifs and folded interaction domains are very common across multicellular life. It is likely that the evolution of affinity in motif-mediated interactions often involves an interplay between specific interactions made by conserved motif residues and nonspecific interactions by nonconserved disordered regions.

Background Plant organelle genomes are a valuable resource for evolutionary biology research, yet their genome architectures, evolutionary patterns and environmental adaptations are poorly understood in many lineages. Rhodiola species is a type of flora mainly distributed in highland habitats, with high medicinal value. Here, we assembled the organelle genomes of three Rhodiola species ( R. wallichiana , R. crenulata and R. sacra ) collected from the Qinghai-Tibet plateau (QTP), and compared their genome structure, gene content, structural rearrangements, sequence transfer and sequence evolution rates. Results The results demonstrated the contrasting evolutionary pattern between plastomes and mitogenomes in three Rhodiola species, with the former possessing more conserved genome structure but faster evolutionary rates of sequence, while the latter exhibiting structural diversity but slower rates of sequence evolution. Some lineage-specific features were observed in Rhodiola mitogenomes, including chromosome fission, gene loss and structural rearrangement. Repeat element analysis shows that the repeats occurring between the two chromosomes may mediate the formation of multichromosomal structure in the mitogenomes of Rhodiola , and this multichromosomal structure may have recently formed. The identification of homologous sequences between plastomes and mitogenomes reveals several unidirectional protein-coding gene transfer events from chloroplasts to mitochondria. Moreover, we found that their organelle genomes contained multiple fragments of nuclear transposable elements (TEs) and exhibited different preferences for TEs insertion type. Genome-wide scans of positive selection identified one gene matR from the mitogenome. Since the matR is crucial for plant growth and development, as well as for respiration and stress responses, our findings suggest that matR may participate in the adaptive response of Rhodiola species to environmental stress of QTP. Conclusion The study analyzed the organelle genomes of three Rhodiola species and demonstrated the contrasting evolutionary pattern between plastomes and mitogenomes. Signals of positive selection were detected in the matR gene of Rhodiola mitogenomes, suggesting the potential role of this gene in Rhodiola adaptation to QTP. Together, the study is expected to enrich the genomic resources and provide valuable insights into the structural dynamics and sequence divergences of Rhodiola species.

Significance We present experimental evidence showing that meiosis is an important source of germline mutations. Because sites of meiotic recombination experience recurrent double-strand breaks at hotspots, recombination has been previously suspected to be mutagenic. Yet inferences made from sequence comparisons have not found strong evidence for a mutagenic effect of recombination. Here, we directly sequenced a large number of single sperm DNA molecules and found more new mutations in molecules with a crossover than in molecules without a recombination event. We also observed that GC alleles are transmitted more often than AT alleles at polymorphic sites. Our data demonstrate that both mutagenesis and biased transmission occur during crossing over in meiosis and are important modifiers of the sequence content at recombination hotspots. Meiosis is a potentially important source of germline mutations, as sites of meiotic recombination experience recurrent double-strand breaks (DSBs). However, evidence for a local mutagenic effect of recombination from population sequence data has been equivocal, likely because mutation is only one of several forces shaping sequence variation. By sequencing large numbers of single crossover molecules obtained from human sperm for two recombination hotspots, we find direct evidence that recombination is mutagenic: Crossovers carry more de novo mutations than nonrecombinant DNA molecules analyzed for the same donors and hotspots. The observed mutations were primarily CG to TA transitions, with a higher frequency of transitions at CpG than non-CpGs sites. This enrichment of mutations at CpG sites at hotspots could predominate in methylated regions involving frequent single-stranded DNA processing as part of DSB repair. In addition, our data set provides evidence that GC alleles are preferentially transmitted during crossing over, opposing mutation, and shows that GC-biased gene conversion (gBGC) predominates over mutation in the sequence evolution of hotspots. These findings are consistent with the idea that gBGC could be an adaptation to counteract the mutational load of recombination.

In the history of life, some phenotypes have been acquired several times independently, through convergent evolution. Recently, lots of genome-scale studies have been devoted to identify nucleotides or amino acids that changed in a convergent manner when the convergent phenotypes evolved. These efforts have had mixed results, probably because of differences in the detection methods, and because of conceptual differences about the definition of a convergent substitution. Some methods contend that substitutions are convergent only if they occur on all branches where the phenotype changed toward the exact same state at a given nucleotide or amino acid position. Others are much looser in their requirements and define a convergent substitution as one that leads the site at which they occur to prefer a phylogeny in which species with the convergent phenotype group together. Here, we suggest to look for convergent shifts in amino acid preferences instead of convergent substitutions to the exact same amino acid. We define as convergent shifts substitutions that occur on all branches where the phenotype changed and such that they correspond to a change in the type of amino acid preferred at this position. We implement the corresponding model into a method named PCOC. We show on simulations that PCOC better recovers convergent shifts than existing methods in terms of sensitivity and specificity. We test it on a plant protein alignment where convergent evolution has been studied in detail and find that our method recovers several previously identified convergent substitutions and proposes credible new candidates.

Phylogenetic inference is of fundamental importance to evolutionary as well as other fields of biology, and molecular sequences have emerged as the primary data for this task. Although many phylogenetic methods have been developed to explicitly take into account substitution models of sequence evolution, such methods could fail due to model misspecification or insufficiency, especially in the face of heterogeneities in substitution processes across sites and among lineages. In this study, we propose to infer topologies of four-taxon trees using deep residual neural networks, a machine learning approach needing no explicit modeling of the subject system and having a record of success in solving complex nonlinear inference problems. We train residual networks on simulated protein sequence data with extensive amino acid substitution heterogeneities. We show that the well-trained residual network predictors can outperform existing state-of-the-art inference methods such as the maximum likelihood method on diverse simulated test data, especially under extensive substitution heterogeneities. Reassuringly, residual network predictors generally agree with existing methods in the trees inferred from real phylogenetic data with known or widely believed topologies. Furthermore, when combined with the quartet puzzling algorithm, residual network predictors can be used to reconstruct trees with more than four taxa. We conclude that deep learning represents a powerful new approach to phylogenetic reconstruction, especially when sequences evolve via heterogeneous substitution processes. We present our best trained predictor in a freely available program named Phylogenetics by Deep Learning (PhyDL, https://gitlab.com/ztzou/phydl; last accessed January 3, 2020).